diff --git a/.github/workflows/push.yml b/.github/workflows/push.yml
index 9780df63fc..66d892efff 100644
--- a/.github/workflows/push.yml
+++ b/.github/workflows/push.yml
@@ -42,6 +42,16 @@ jobs:
           -src_vocab /tmp/onmt.vocab.src \
           -tgt_vocab /tmp/onmt.vocab.tgt \
           && rm -rf /tmp/sample
+    - name: Test vocabulary build with features
+      run: |
+        python onmt/bin/build_vocab.py \
+          -config data/features_data.yaml \
+          -save_data /tmp/onmt_feat \
+          -src_vocab /tmp/onmt_feat.vocab.src \
+          -tgt_vocab /tmp/onmt_feat.vocab.tgt \
+          -src_feats_vocab '{"feat0": "/tmp/onmt_feat.vocab.feat0"}' \
+          -n_sample -1 \
+          && rm -rf /tmp/sample
     - name: Test field/transform dump
       run: |
         # The dumped fields are used later when testing tools
@@ -169,6 +179,26 @@ jobs:
           -state_dim 256 \
           -n_steps 10 \
           -n_node 64
+    - name: Testing training with features
+      run: |
+        python onmt/bin/train.py \
+          -config data/features_data.yaml \
+          -src_vocab /tmp/onmt_feat.vocab.src \
+          -tgt_vocab /tmp/onmt_feat.vocab.tgt \
+          -src_feats_vocab '{"feat0": "/tmp/onmt_feat.vocab.feat0"}' \
+          -src_vocab_size 1000 -tgt_vocab_size 1000 \
+          -rnn_size 2 -batch_size 10 \
+          -word_vec_size 5 -rnn_size 10 \
+          -report_every 5 -train_steps 10 \
+          -save_model /tmp/onmt.model \
+          -save_checkpoint_steps 10
+    - name: Testing translation with features
+      run: |
+        python translate.py \
+            -model /tmp/onmt.model_step_10.pt \
+            -src data/data_features/src-test.txt \
+            -src_feats "{'feat0': 'data/data_features/src-test.feat0'}" \
+            -verbose
     - name: Test RNN translation
       run: |
         head data/src-test.txt > /tmp/src-test.txt
diff --git a/data/data_features/src-test.feat0 b/data/data_features/src-test.feat0
new file mode 100644
index 0000000000..4ab4a9e651
--- /dev/null
+++ b/data/data_features/src-test.feat0
@@ -0,0 +1 @@
+C B A B
\ No newline at end of file
diff --git a/data/data_features/src-test.txt b/data/data_features/src-test.txt
new file mode 100644
index 0000000000..0cc723ce39
--- /dev/null
+++ b/data/data_features/src-test.txt
@@ -0,0 +1 @@
+she is a hard-working.
\ No newline at end of file
diff --git a/data/data_features/src-train.feat0 b/data/data_features/src-train.feat0
new file mode 100644
index 0000000000..7e189f2c33
--- /dev/null
+++ b/data/data_features/src-train.feat0
@@ -0,0 +1,3 @@
+A A A A B A A A C
+A B C D E
+C B A B
\ No newline at end of file
diff --git a/data/data_features/src-train.txt b/data/data_features/src-train.txt
new file mode 100644
index 0000000000..8a3ec35c2b
--- /dev/null
+++ b/data/data_features/src-train.txt
@@ -0,0 +1,3 @@
+however, according to the logs, she is a hard-working.
+however, according to the logs,
+she is a hard-working.
\ No newline at end of file
diff --git a/data/data_features/src-val.feat0 b/data/data_features/src-val.feat0
new file mode 100644
index 0000000000..4ab4a9e651
--- /dev/null
+++ b/data/data_features/src-val.feat0
@@ -0,0 +1 @@
+C B A B
\ No newline at end of file
diff --git a/data/data_features/src-val.txt b/data/data_features/src-val.txt
new file mode 100644
index 0000000000..0cc723ce39
--- /dev/null
+++ b/data/data_features/src-val.txt
@@ -0,0 +1 @@
+she is a hard-working.
\ No newline at end of file
diff --git a/data/data_features/tgt-train.txt b/data/data_features/tgt-train.txt
new file mode 100644
index 0000000000..8a3ec35c2b
--- /dev/null
+++ b/data/data_features/tgt-train.txt
@@ -0,0 +1,3 @@
+however, according to the logs, she is a hard-working.
+however, according to the logs,
+she is a hard-working.
\ No newline at end of file
diff --git a/data/data_features/tgt-val.txt b/data/data_features/tgt-val.txt
new file mode 100644
index 0000000000..0cc723ce39
--- /dev/null
+++ b/data/data_features/tgt-val.txt
@@ -0,0 +1 @@
+she is a hard-working.
\ No newline at end of file
diff --git a/data/features_data.yaml b/data/features_data.yaml
new file mode 100644
index 0000000000..fa9b665f9c
--- /dev/null
+++ b/data/features_data.yaml
@@ -0,0 +1,11 @@
+# Corpus opts:
+data:
+    corpus_1:
+        path_src: data/data_features/src-train.txt
+        path_tgt: data/data_features/tgt-train.txt
+        src_feats:
+            feat0: data/data_features/src-train.feat0
+        transforms: [filterfeats, inferfeats]
+    valid:
+        path_src: data/data_features/src-val.txt
+        path_tgt: data/data_features/tgt-val.txt
diff --git a/docs/source/FAQ.md b/docs/source/FAQ.md
index f40fada251..8f618f6c6e 100644
--- a/docs/source/FAQ.md
+++ b/docs/source/FAQ.md
@@ -477,3 +477,73 @@ Training options to perform vocabulary update are:
 * `-update_vocab`: set this option
 * `-reset_optim`: set the value to "states"
 * `-train_from`: checkpoint path
+
+
+## How can I use source word features?
+
+Extra information can be added to the words in the source sentences by defining word features. 
+
+Features should be defined in a separate file using blank spaces as a separator and with each row corresponding to a source sentence. An example of the input files:
+
+data.src
+```
+however, according to the logs, she is hard-working.
+```
+
+feat0.txt
+```
+A C C C C A A B
+```
+
+**Notes**
+- Prior tokenization is not necessary, features will be inferred by using the `FeatInferTransform` transform.
+- `FilterFeatsTransform` and `FeatInferTransform` are required in order to ensure the functionality.
+- Not possible to do shared embeddings (at least with `feat_merge: concat` method)
+
+Sample config file:
+
+```
+data:
+    dummy:
+        path_src: data/train/data.src
+        path_tgt: data/train/data.tgt
+        src_feats:
+            feat_0: data/train/data.src.feat_0
+            feat_1: data/train/data.src.feat_1
+        transforms: [filterfeats, onmt_tokenize, inferfeats, filtertoolong]
+        weight: 1
+    valid:
+        path_src: data/valid/data.src
+        path_tgt: data/valid/data.tgt
+        src_feats:
+            feat_0: data/valid/data.src.feat_0
+            feat_1: data/valid/data.src.feat_1
+        transforms: [filterfeats, onmt_tokenize, inferfeats]
+
+# # Vocab opts
+src_vocab: exp/data.vocab.src
+tgt_vocab: exp/data.vocab.tgt
+src_feats_vocab: 
+    feat_0: exp/data.vocab.feat_0
+    feat_1: exp/data.vocab.feat_1
+feat_merge: "sum"
+
+```
+
+During inference you can pass features by using the `--src_feats` argument. `src_feats` is expected to be a Python like dict, mapping feature name with its data file.
+
+```
+{'feat_0': '../data.txt.feats0', 'feat_1': '../data.txt.feats1'}
+```
+
+**Important note!** During inference, input sentence is expected to be tokenized. Therefore feature inferring should be handled prior to running the translate command. Example:
+
+```bash
+python translate.py -model model_step_10.pt -src ../data.txt.tok -output ../data.out --src_feats "{'feat_0': '../data.txt.feats0', 'feat_1': '../data.txt.feats1'}"
+```
+
+When using the Transformer architecture make sure the following options are appropriately set:
+
+- `src_word_vec_size` and `tgt_word_vec_size` or `word_vec_size`
+- `feat_merge`: how to handle features vecs
+- `feat_vec_size` and maybe `feat_vec_exponent`
diff --git a/onmt/bin/build_vocab.py b/onmt/bin/build_vocab.py
index e106d92180..ed510f09d2 100644
--- a/onmt/bin/build_vocab.py
+++ b/onmt/bin/build_vocab.py
@@ -32,11 +32,13 @@ def build_vocab_main(opts):
     transforms = make_transforms(opts, transforms_cls, fields)
 
     logger.info(f"Counter vocab from {opts.n_sample} samples.")
-    src_counter, tgt_counter = build_vocab(
+    src_counter, tgt_counter, src_feats_counter = build_vocab(
         opts, transforms, n_sample=opts.n_sample)
 
     logger.info(f"Counters src:{len(src_counter)}")
     logger.info(f"Counters tgt:{len(tgt_counter)}")
+    for feat_name, feat_counter in src_feats_counter.items():
+        logger.info(f"Counters {feat_name}:{len(feat_counter)}")
 
     def save_counter(counter, save_path):
         check_path(save_path, exist_ok=opts.overwrite, log=logger.warning)
@@ -52,6 +54,9 @@ def save_counter(counter, save_path):
     else:
         save_counter(src_counter, opts.src_vocab)
         save_counter(tgt_counter, opts.tgt_vocab)
+    
+    for k, v in src_feats_counter.items():
+        save_counter(v, opts.src_feats_vocab[k])
 
 
 def _get_parser():
diff --git a/onmt/bin/translate.py b/onmt/bin/translate.py
index 0b5434f89a..4e3e126ae2 100755
--- a/onmt/bin/translate.py
+++ b/onmt/bin/translate.py
@@ -6,6 +6,7 @@
 
 import onmt.opts as opts
 from onmt.utils.parse import ArgumentParser
+from collections import defaultdict
 
 
 def translate(opt):
@@ -15,12 +16,21 @@ def translate(opt):
     translator = build_translator(opt, logger=logger, report_score=True)
     src_shards = split_corpus(opt.src, opt.shard_size)
     tgt_shards = split_corpus(opt.tgt, opt.shard_size)
-    shard_pairs = zip(src_shards, tgt_shards)
-
-    for i, (src_shard, tgt_shard) in enumerate(shard_pairs):
+    features_shards = []
+    features_names = []
+    for feat_name, feat_path in opt.src_feats.items():
+        features_shards.append(split_corpus(feat_path, opt.shard_size))
+        features_names.append(feat_name)
+    shard_pairs = zip(src_shards, tgt_shards, *features_shards)
+
+    for i, (src_shard, tgt_shard, *features_shard) in enumerate(shard_pairs):
+        features_shard_ = defaultdict(list)
+        for j, x in enumerate(features_shard):
+            features_shard_[features_names[j]] = x
         logger.info("Translating shard %d." % i)
         translator.translate(
             src=src_shard,
+            src_feats=features_shard_,
             tgt=tgt_shard,
             batch_size=opt.batch_size,
             batch_type=opt.batch_type,
diff --git a/onmt/constants.py b/onmt/constants.py
index fb6afb0252..2d5864137b 100644
--- a/onmt/constants.py
+++ b/onmt/constants.py
@@ -22,6 +22,7 @@ class CorpusName(object):
 class SubwordMarker(object):
     SPACER = '▁'
     JOINER = '￭'
+    CASE_MARKUP = ["｟mrk_case_modifier_C｠", "｟mrk_begin_case_region_U｠", "｟mrk_end_case_region_U｠"]
 
 
 class ModelTask(object):
diff --git a/onmt/inputters/corpus.py b/onmt/inputters/corpus.py
index c8a559f9f8..87da65139b 100644
--- a/onmt/inputters/corpus.py
+++ b/onmt/inputters/corpus.py
@@ -7,10 +7,11 @@
 from torchtext.data import Dataset as TorchtextDataset, \
     Example as TorchtextExample
 
-from collections import Counter
+from collections import Counter, defaultdict
 from contextlib import contextmanager
 
 import multiprocessing as mp
+from collections import defaultdict
 
 
 @contextmanager
@@ -70,10 +71,20 @@ def _process(item, is_train):
             example, is_train=is_train, corpus_name=cid)
         if maybe_example is None:
             return None
-        maybe_example['src'] = ' '.join(maybe_example['src'])
-        maybe_example['tgt'] = ' '.join(maybe_example['tgt'])
+
+        maybe_example['src'] = {"src": ' '.join(maybe_example['src'])}
+
+        # Make features part of src as in TextMultiField
+        # {'src': {'src': ..., 'feat1': ...., 'feat2': ....}}
+        if 'src_feats' in maybe_example:
+            for feat_name, feat_value in maybe_example['src_feats'].items():
+                maybe_example['src'][feat_name] = ' '.join(feat_value)
+            del maybe_example["src_feats"]
+
+        maybe_example['tgt'] = {"tgt": ' '.join(maybe_example['tgt'])}
         if 'align' in maybe_example:
             maybe_example['align'] = ' '.join(maybe_example['align'])
+
         return maybe_example
 
     def _maybe_add_dynamic_dict(self, example, fields):
@@ -107,12 +118,13 @@ def __call__(self, bucket):
 class ParallelCorpus(object):
     """A parallel corpus file pair that can be loaded to iterate."""
 
-    def __init__(self, name, src, tgt, align=None):
+    def __init__(self, name, src, tgt, align=None, src_feats=None):
         """Initialize src & tgt side file path."""
         self.id = name
         self.src = src
         self.tgt = tgt
         self.align = align
+        self.src_feats = src_feats
 
     def load(self, offset=0, stride=1):
         """
@@ -120,10 +132,18 @@ def load(self, offset=0, stride=1):
         `offset` and `stride` allow to iterate only on every
         `stride` example, starting from `offset`.
         """
+        if self.src_feats:
+            features_names = []
+            features_files = []
+            for feat_name, feat_path in self.src_feats.items():
+                features_names.append(feat_name)
+                features_files.append(open(feat_path, mode='rb'))
+        else:
+            features_files = []
         with exfile_open(self.src, mode='rb') as fs,\
                 exfile_open(self.tgt, mode='rb') as ft,\
                 exfile_open(self.align, mode='rb') as fa:
-            for i, (sline, tline, align) in enumerate(zip(fs, ft, fa)):
+            for i, (sline, tline, align, *features) in enumerate(zip(fs, ft, fa, *features_files)):
                 if (i % stride) == offset:
                     sline = sline.decode('utf-8')
                     tline = tline.decode('utf-8')
@@ -133,12 +153,18 @@ def load(self, offset=0, stride=1):
                     }
                     if align is not None:
                         example['align'] = align.decode('utf-8')
+                    if features:
+                        example["src_feats"] = dict()
+                        for j, feat in enumerate(features):
+                            example["src_feats"][features_names[j]] = feat.decode("utf-8")
                     yield example
+        for f in features_files:
+            f.close()
 
     def __str__(self):
         cls_name = type(self).__name__
-        return '{}({}, {}, align={})'.format(
-            cls_name, self.src, self.tgt, self.align)
+        return '{}({}, {}, align={}, src_feats={})'.format(
+            cls_name, self.src, self.tgt, self.align, self.src_feats)
 
 
 def get_corpora(opts, is_train=False):
@@ -150,14 +176,16 @@ def get_corpora(opts, is_train=False):
                     corpus_id,
                     corpus_dict["path_src"],
                     corpus_dict["path_tgt"],
-                    corpus_dict["path_align"])
+                    corpus_dict["path_align"],
+                    corpus_dict["src_feats"])
     else:
         if CorpusName.VALID in opts.data.keys():
             corpora_dict[CorpusName.VALID] = ParallelCorpus(
                 CorpusName.VALID,
                 opts.data[CorpusName.VALID]["path_src"],
                 opts.data[CorpusName.VALID]["path_tgt"],
-                opts.data[CorpusName.VALID]["path_align"])
+                opts.data[CorpusName.VALID]["path_align"],
+                opts.data[CorpusName.VALID]["src_feats"])
         else:
             return None
     return corpora_dict
@@ -193,6 +221,9 @@ def _tokenize(self, stream):
             example['src'], example['tgt'] = src, tgt
             if 'align' in example:
                 example['align'] = example['align'].strip('\n').split()
+            if 'src_feats' in example:
+                for k in example['src_feats'].keys():
+                    example['src_feats'][k] = example['src_feats'][k].strip('\n').split()
             yield example
 
     def _transform(self, stream):
@@ -286,6 +317,7 @@ def build_sub_vocab(corpora, transforms, opts, n_sample, stride, offset):
     """Build vocab on (strided) subpart of the data."""
     sub_counter_src = Counter()
     sub_counter_tgt = Counter()
+    sub_counter_src_feats =  defaultdict(Counter)
     datasets_iterables = build_corpora_iters(
         corpora, transforms, opts.data,
         skip_empty_level=opts.skip_empty_level,
@@ -297,7 +329,10 @@ def build_sub_vocab(corpora, transforms, opts, n_sample, stride, offset):
                 if opts.dump_samples:
                     build_sub_vocab.queues[c_name][offset].put("blank")
                 continue
-            src_line, tgt_line = maybe_example['src'], maybe_example['tgt']
+            src_line, tgt_line = maybe_example['src']['src'], maybe_example['tgt']['tgt']
+            for feat_name, feat_line in maybe_example["src"].items():
+                if feat_name != "src":
+                    sub_counter_src_feats[feat_name].update(feat_line.split(' '))
             sub_counter_src.update(src_line.split(' '))
             sub_counter_tgt.update(tgt_line.split(' '))
             if opts.dump_samples:
@@ -309,7 +344,7 @@ def build_sub_vocab(corpora, transforms, opts, n_sample, stride, offset):
                 break
         if opts.dump_samples:
             build_sub_vocab.queues[c_name][offset].put("break")
-    return sub_counter_src, sub_counter_tgt
+    return sub_counter_src, sub_counter_tgt, sub_counter_src_feats
 
 
 def init_pool(queues):
@@ -333,6 +368,7 @@ def build_vocab(opts, transforms, n_sample=3):
     corpora = get_corpora(opts, is_train=True)
     counter_src = Counter()
     counter_tgt = Counter()
+    counter_src_feats =  defaultdict(Counter)
     from functools import partial
     queues = {c_name: [mp.Queue(opts.vocab_sample_queue_size)
                        for i in range(opts.num_threads)]
@@ -349,13 +385,14 @@ def build_vocab(opts, transforms, n_sample=3):
         func = partial(
             build_sub_vocab, corpora, transforms,
             opts, n_sample, opts.num_threads)
-        for sub_counter_src, sub_counter_tgt in p.imap(
+        for sub_counter_src, sub_counter_tgt, sub_counter_src_feats in p.imap(
                 func, range(0, opts.num_threads)):
             counter_src.update(sub_counter_src)
             counter_tgt.update(sub_counter_tgt)
+            counter_src_feats.update(sub_counter_src_feats)
     if opts.dump_samples:
         write_process.join()
-    return counter_src, counter_tgt
+    return counter_src, counter_tgt, counter_src_feats
 
 
 def save_transformed_sample(opts, transforms, n_sample=3):
@@ -387,7 +424,7 @@ def save_transformed_sample(opts, transforms, n_sample=3):
                 maybe_example = DatasetAdapter._process(item, is_train=True)
                 if maybe_example is None:
                     continue
-                src_line, tgt_line = maybe_example['src'], maybe_example['tgt']
+                src_line, tgt_line = maybe_example['src']['src'], maybe_example['tgt']['tgt']
                 f_src.write(src_line + '\n')
                 f_tgt.write(tgt_line + '\n')
                 if n_sample > 0 and i >= n_sample:
diff --git a/onmt/inputters/dataset_base.py b/onmt/inputters/dataset_base.py
index aeec428aaf..65322d9a4c 100644
--- a/onmt/inputters/dataset_base.py
+++ b/onmt/inputters/dataset_base.py
@@ -41,7 +41,7 @@ def _dynamic_dict(example, src_field, tgt_field):
         ``example``, changed as described.
     """
 
-    src = src_field.tokenize(example["src"])
+    src = src_field.tokenize(example["src"]["src"])
     # make a small vocab containing just the tokens in the source sequence
     unk = src_field.unk_token
     pad = src_field.pad_token
@@ -60,7 +60,7 @@ def _dynamic_dict(example, src_field, tgt_field):
     example["src_ex_vocab"] = src_ex_vocab
 
     if "tgt" in example:
-        tgt = tgt_field.tokenize(example["tgt"])
+        tgt = tgt_field.tokenize(example["tgt"]["tgt"])
         mask = torch.LongTensor(
             [unk_idx] + [src_ex_vocab.stoi[w] for w in tgt] + [unk_idx])
         example["alignment"] = mask
@@ -116,7 +116,7 @@ def __init__(self, fields, readers, data, sort_key, filter_pred=None):
         self.sort_key = sort_key
         can_copy = 'src_map' in fields and 'alignment' in fields
 
-        read_iters = [r.read(dat[1], dat[0]) for r, dat in zip(readers, data)]
+        read_iters = [r.read(dat, name, feats) for r, (name, dat, feats) in zip(readers, data)]
 
         # self.src_vocabs is used in collapse_copy_scores and Translator.py
         self.src_vocabs = []
@@ -162,5 +162,5 @@ def config(fields):
         for name, field in fields:
             if field["data"] is not None:
                 readers.append(field["reader"])
-                data.append((name, field["data"]))
+                data.append((name, field["data"], field["features"]))
         return readers, data
diff --git a/onmt/inputters/fields.py b/onmt/inputters/fields.py
index 50c4e6c17f..5f41a3a01f 100644
--- a/onmt/inputters/fields.py
+++ b/onmt/inputters/fields.py
@@ -8,11 +8,10 @@
 
 
 def _get_dynamic_fields(opts):
-    # NOTE: not support nfeats > 0 yet
-    src_nfeats = 0
-    tgt_nfeats = 0
+    # NOTE: not support tgt feats yet
+    tgt_feats = None
     with_align = hasattr(opts, 'lambda_align') and opts.lambda_align > 0.0
-    fields = get_fields('text', src_nfeats, tgt_nfeats,
+    fields = get_fields('text', opts.src_feats_vocab, tgt_feats,
                         dynamic_dict=opts.copy_attn,
                         src_truncate=opts.src_seq_length_trunc,
                         tgt_truncate=opts.tgt_seq_length_trunc,
@@ -33,6 +32,12 @@ def build_dynamic_fields(opts, src_specials=None, tgt_specials=None):
         opts.src_vocab, 'src', counters,
         min_freq=opts.src_words_min_frequency)
 
+    if opts.src_feats_vocab:
+        for feat_name, filepath in opts.src_feats_vocab.items():
+            _, _ = _load_vocab(
+                filepath, feat_name, counters,
+                min_freq=0)
+
     if opts.tgt_vocab:
         _tgt_vocab, _tgt_vocab_size = _load_vocab(
             opts.tgt_vocab, 'tgt', counters,
diff --git a/onmt/inputters/inputter.py b/onmt/inputters/inputter.py
index f6b5c747d0..ffd8c77fb1 100644
--- a/onmt/inputters/inputter.py
+++ b/onmt/inputters/inputter.py
@@ -111,8 +111,8 @@ def get_task_spec_tokens(data_task, pad, bos, eos):
 
 def get_fields(
     src_data_type,
-    n_src_feats,
-    n_tgt_feats,
+    src_feats,
+    tgt_feats,
     pad=DefaultTokens.PAD,
     bos=DefaultTokens.BOS,
     eos=DefaultTokens.EOS,
@@ -125,11 +125,11 @@ def get_fields(
     """
     Args:
         src_data_type: type of the source input. Options are [text].
-        n_src_feats (int): the number of source features (not counting tokens)
+        src_feats (Optional[Dict]): source features dict containing their names
             to create a :class:`torchtext.data.Field` for. (If
             ``src_data_type=="text"``, these fields are stored together
             as a ``TextMultiField``).
-        n_tgt_feats (int): See above.
+        tgt_feats (Optional[Dict]): See above.
         pad (str): Special pad symbol. Used on src and tgt side.
         bos (str): Special beginning of sequence symbol. Only relevant
             for tgt.
@@ -158,7 +158,7 @@ def get_fields(
     task_spec_tokens = get_task_spec_tokens(data_task, pad, bos, eos)
 
     src_field_kwargs = {
-        "n_feats": n_src_feats,
+        "feats": src_feats,
         "include_lengths": True,
         "pad": task_spec_tokens["src"]["pad"],
         "bos": task_spec_tokens["src"]["bos"],
@@ -169,7 +169,7 @@ def get_fields(
     fields["src"] = fields_getters[src_data_type](**src_field_kwargs)
 
     tgt_field_kwargs = {
-        "n_feats": n_tgt_feats,
+        "feats": tgt_feats,
         "include_lengths": False,
         "pad": task_spec_tokens["tgt"]["pad"],
         "bos": task_spec_tokens["tgt"]["bos"],
diff --git a/onmt/inputters/text_dataset.py b/onmt/inputters/text_dataset.py
index a0621f6407..a55d2593b2 100644
--- a/onmt/inputters/text_dataset.py
+++ b/onmt/inputters/text_dataset.py
@@ -9,7 +9,7 @@
 
 
 class TextDataReader(DataReaderBase):
-    def read(self, sequences, side):
+    def read(self, sequences, side, features={}):
         """Read text data from disk.
 
         Args:
@@ -17,6 +17,9 @@ def read(self, sequences, side):
                 path to text file or iterable of the actual text data.
             side (str): Prefix used in return dict. Usually
                 ``"src"`` or ``"tgt"``.
+            features: (Dict[str or Iterable[str]]):
+                dictionary mapping feature names with the path to feature
+                file or iterable of the actual feature data.
 
         Yields:
             dictionaries whose keys are the names of fields and whose
@@ -25,10 +28,25 @@ def read(self, sequences, side):
         """
         if isinstance(sequences, str):
             sequences = DataReaderBase._read_file(sequences)
-        for i, seq in enumerate(sequences):
+
+        features_names = []
+        features_values = []
+        for feat_name, v in features.items():
+            features_names.append(feat_name)
+            if isinstance(v, str):
+                features_values.append(DataReaderBase._read_file(features))
+            else:
+                features_values.append(v)  
+        for i, (seq, *feats) in enumerate(zip(sequences, *features_values)):
+            ex_dict = {}
             if isinstance(seq, bytes):
                 seq = seq.decode("utf-8")
-            yield {side: seq, "indices": i}
+            ex_dict[side] = seq
+            for i, f in enumerate(feats):
+                if isinstance(f, bytes):
+                    f = f.decode("utf-8")
+                ex_dict[features_names[i]] = f
+            yield {side: ex_dict, "indices": i}
 
 
 def text_sort_key(ex):
@@ -38,6 +56,7 @@ def text_sort_key(ex):
     return len(ex.src[0])
 
 
+# Legacy function. Currently it only truncates input if truncate is set.
 # mix this with partial
 def _feature_tokenize(
         string, layer=0, tok_delim=None, feat_delim=None, truncate=None):
@@ -140,8 +159,7 @@ def preprocess(self, x):
                 lists of tokens/feature tags for the sentence. The output
                 is ordered like ``self.fields``.
         """
-
-        return [f.preprocess(x) for _, f in self.fields]
+        return [f.preprocess(x[fn]) for fn, f in self.fields]
 
     def __getitem__(self, item):
         return self.fields[item]
@@ -152,7 +170,7 @@ def text_fields(**kwargs):
 
     Args:
         base_name (str): Name associated with the field.
-        n_feats (int): Number of word level feats (not counting the tokens)
+        feats (Optional[Dict]): Word level feats
         include_lengths (bool): Optionally return the sequence lengths.
         pad (str, optional): Defaults to ``"<blank>"``.
         bos (str or NoneType, optional): Defaults to ``"<s>"``.
@@ -163,7 +181,7 @@ def text_fields(**kwargs):
         TextMultiField
     """
 
-    n_feats = kwargs["n_feats"]
+    feats = kwargs["feats"]
     include_lengths = kwargs["include_lengths"]
     base_name = kwargs["base_name"]
     pad = kwargs.get("pad", DefaultTokens.PAD)
@@ -171,20 +189,36 @@ def text_fields(**kwargs):
     eos = kwargs.get("eos", DefaultTokens.EOS)
     truncate = kwargs.get("truncate", None)
     fields_ = []
-    feat_delim = u"￨" if n_feats > 0 else None
-    for i in range(n_feats + 1):
-        name = base_name + "_feat_" + str(i - 1) if i > 0 else base_name
-        tokenize = partial(
-            _feature_tokenize,
-            layer=i,
-            truncate=truncate,
-            feat_delim=feat_delim)
-        use_len = i == 0 and include_lengths
-        feat = Field(
-            init_token=bos, eos_token=eos,
-            pad_token=pad, tokenize=tokenize,
-            include_lengths=use_len)
-        fields_.append((name, feat))
+
+    feat_delim = None #u"￨" if n_feats > 0 else None
+
+    # Base field
+    tokenize = partial(
+        _feature_tokenize,
+        layer=None,
+        truncate=truncate,
+        feat_delim=feat_delim)
+    feat = Field(
+        init_token=bos, eos_token=eos,
+        pad_token=pad, tokenize=tokenize,
+        include_lengths=include_lengths)
+    fields_.append((base_name, feat))
+
+    # Feats fields
+    if feats:
+        for feat_name in feats.keys():
+            # Legacy function, it is not really necessary
+            tokenize = partial(
+                _feature_tokenize,
+                layer=None,
+                truncate=truncate,
+                feat_delim=feat_delim)
+            feat = Field(
+                init_token=bos, eos_token=eos,
+                pad_token=pad, tokenize=tokenize,
+                include_lengths=False)
+            fields_.append((feat_name, feat))
+
     assert fields_[0][0] == base_name  # sanity check
     field = TextMultiField(fields_[0][0], fields_[0][1], fields_[1:])
     return field
diff --git a/onmt/opts.py b/onmt/opts.py
index ec66f14e95..4c37ab952d 100644
--- a/onmt/opts.py
+++ b/onmt/opts.py
@@ -132,6 +132,11 @@ def _add_dynamic_fields_opts(parser, build_vocab_only=False):
     group.add("-share_vocab", "--share_vocab", action="store_true",
               help="Share source and target vocabulary.")
 
+    group.add("-src_feats_vocab", "--src_feats_vocab",
+              help=("List of paths to save" if build_vocab_only else "List of paths to")
+              + " src features vocabulary files. "
+              "Files format: one <word> or <word>\t<count> per line.")
+
     if not build_vocab_only:
         group.add("-src_vocab_size", "--src_vocab_size",
                   type=int, default=50000,
@@ -755,6 +760,9 @@ def translate_opts(parser):
     group.add('--src', '-src', required=True,
               help="Source sequence to decode (one line per "
                    "sequence)")
+    group.add("-src_feats", "--src_feats", required=False,
+              help="Source sequence features (dict format). "
+                    "Ex: {'feat_0': '../data.txt.feats0', 'feat_1': '../data.txt.feats1'}")
     group.add('--tgt', '-tgt',
               help='True target sequence (optional)')
     group.add('--tgt_prefix', '-tgt_prefix', action='store_true',
diff --git a/onmt/tests/pull_request_chk.sh b/onmt/tests/pull_request_chk.sh
index b282cc7f1e..70cd76823a 100755
--- a/onmt/tests/pull_request_chk.sh
+++ b/onmt/tests/pull_request_chk.sh
@@ -67,10 +67,22 @@ PYTHONPATH=${PROJECT_ROOT}:${PYTHONPATH} ${PYTHON} onmt/bin/build_vocab.py \
             -save_data $TMP_OUT_DIR/onmt \
             -src_vocab $TMP_OUT_DIR/onmt.vocab.src \
             -tgt_vocab $TMP_OUT_DIR/onmt.vocab.tgt \
-            -n_sample 5000 >> ${LOG_FILE} 2>&1
+            -n_sample 5000 -overwrite >> ${LOG_FILE} 2>&1
 [ "$?" -eq 0 ] || error_exit
 echo "Succeeded" | tee -a ${LOG_FILE}
-rm -r $TMP_OUT_DIR/sample
+rm -f -r $TMP_OUT_DIR/sample
+
+echo -n "[+] Testing vocabulary building with features..."
+PYTHONPATH=${PROJECT_ROOT}:${PYTHONPATH} ${PYTHON} onmt/bin/build_vocab.py \
+            -config ${DATA_DIR}/features_data.yaml \
+            -save_data $TMP_OUT_DIR/onmt_feat \
+            -src_vocab $TMP_OUT_DIR/onmt_feat.vocab.src \
+            -tgt_vocab $TMP_OUT_DIR/onmt_feat.vocab.tgt \
+            -src_feats_vocab '{"feat0": "${TMP_OUT_DIR}/onmt_feat.vocab.feat0"}' \
+            -n_sample -1  -overwrite>> ${LOG_FILE} 2>&1
+[ "$?" -eq 0 ] || error_exit
+echo "Succeeded" | tee -a ${LOG_FILE}
+rm -f -r $TMP_OUT_DIR/sample
 
 #
 # Training test
@@ -254,8 +266,24 @@ ${PYTHON} onmt/bin/train.py \
 [ "$?" -eq 0 ] || error_exit
 echo "Succeeded" | tee -a ${LOG_FILE}
 
-rm $TMP_OUT_DIR/onmt.vocab*
-rm $TMP_OUT_DIR/onmt.model*
+echo -n "  [+] Testing training with features..."
+${PYTHON} onmt/bin/train.py \
+            -config ${DATA_DIR}/features_data.yaml \
+            -src_vocab $TMP_OUT_DIR/onmt_feat.vocab.src \
+            -tgt_vocab $TMP_OUT_DIR/onmt_feat.vocab.tgt \
+            -src_feats_vocab '{"feat0": "${TMP_OUT_DIR}/onmt_feat.vocab.feat0"}' \
+            -src_vocab_size 1000 -tgt_vocab_size 1000 \
+            -rnn_size 2 -batch_size 10 \
+            -word_vec_size 5 -rnn_size 10 \
+            -report_every 5 -train_steps 10 \
+            -save_model $TMP_OUT_DIR/onmt.features.model \
+            -save_checkpoint_steps 10 >> ${LOG_FILE} 2>&1
+[ "$?" -eq 0 ] || error_exit
+echo "Succeeded" | tee -a ${LOG_FILE}
+
+rm -f $TMP_OUT_DIR/onmt.vocab*
+rm -f $TMP_OUT_DIR/onmt.model*
+rm -f $TMP_OUT_DIR/onmt_feat.vocab.*
 
 #
 # Translation test
@@ -269,6 +297,16 @@ ${PYTHON} translate.py -model ${TEST_DIR}/test_model.pt -src $TMP_OUT_DIR/src-te
 echo "Succeeded" | tee -a ${LOG_FILE}
 rm $TMP_OUT_DIR/src-test.txt
 
+echo -n "  [+] Testing NMT translation with features..."
+${PYTHON} translate.py \
+            -model ${TMP_OUT_DIR}/onmt.features.model_step_10.pt \
+            -src ${DATA_DIR}/data_features/src-test.txt \
+            -src_feats "{'feat0': '${DATA_DIR}/data_features/src-test.feat0'}" \
+            -verbose >> ${LOG_FILE} 2>&1
+[ "$?" -eq 0 ] || error_exit
+echo "Succeeded" | tee -a ${LOG_FILE}
+rm -f $TMP_OUT_DIR/onmt.features.model*
+
 echo -n "  [+] Testing NMT ensemble translation..."
 head ${DATA_DIR}/src-test.txt > $TMP_OUT_DIR/src-test.txt
 ${PYTHON} translate.py -model ${TEST_DIR}/test_model.pt ${TEST_DIR}/test_model.pt \
diff --git a/onmt/tests/test_subword_marker.py b/onmt/tests/test_subword_marker.py
index e827d52ffa..1b8337b56e 100644
--- a/onmt/tests/test_subword_marker.py
+++ b/onmt/tests/test_subword_marker.py
@@ -2,6 +2,7 @@
 
 from onmt.transforms.bart import word_start_finder
 from onmt.utils.alignment import subword_map_by_joiner, subword_map_by_spacer
+from onmt.constants import DefaultTokens, SubwordMarker
 
 
 class TestWordStartFinder(unittest.TestCase):
@@ -37,7 +38,25 @@ class TestSubwordGroup(unittest.TestCase):
     def test_subword_group_joiner(self):
         data_in = ['however', '￭,', 'according', 'to', 'the', 'logs', '￭,', 'she', 'is', 'hard', '￭-￭', 'working', '￭.']  # noqa: E501
         true_out = [0, 0, 1, 2, 3, 4, 4, 5, 6, 7, 7, 7, 7]
-        out = subword_map_by_joiner(data_in)
+        out = subword_map_by_joiner(data_in, marker=SubwordMarker.JOINER, case_markup=SubwordMarker.CASE_MARKUP)
+        self.assertEqual(out, true_out)
+
+    def test_subword_group_joiner_with_case_markup(self):
+        data_in = ['｟mrk_case_modifier_C｠', 'however', '￭,', 'according', 'to', 'the', 'logs', '￭,', '｟mrk_begin_case_region_U｠', 'she', 'is', 'hard', '￭-￭', 'working', '￭.', '｟mrk_end_case_region_U｠']  # noqa: E501
+        true_out = [0, 0, 0, 1, 2, 3, 4, 4, 5, 5, 6, 7, 7, 7, 7, 7]
+        out = subword_map_by_joiner(data_in, marker=SubwordMarker.JOINER, case_markup=SubwordMarker.CASE_MARKUP)
+        self.assertEqual(out, true_out)
+
+    def test_subword_group_joiner_with_new_joiner(self):
+        data_in = ['｟mrk_case_modifier_C｠', 'however', '￭', ',', 'according', 'to', 'the', 'logs', '￭', ',', '｟mrk_begin_case_region_U｠', 'she', 'is', 'hard', '￭', '-', '￭', 'working', '￭', '.', '｟mrk_end_case_region_U｠']  # noqa: E501
+        true_out = [0, 0, 0, 0, 1, 2, 3, 4, 4, 4, 5, 5, 6, 7, 7, 7, 7, 7, 7, 7, 7]
+        out = subword_map_by_joiner(data_in, marker=SubwordMarker.JOINER, case_markup=SubwordMarker.CASE_MARKUP)
+        self.assertEqual(out, true_out)
+
+    def test_subword_group_naive(self):
+        data_in = ['however', ',', 'according', 'to', 'the', 'logs', ',', 'she', 'is', 'hard', '-', 'working', '.']  # noqa: E501
+        true_out = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
+        out = subword_map_by_joiner(data_in, marker=SubwordMarker.JOINER, case_markup=SubwordMarker.CASE_MARKUP)
         self.assertEqual(out, true_out)
 
     def test_subword_group_spacer(self):
@@ -50,6 +69,18 @@ def test_subword_group_spacer(self):
         no_dummy_out = subword_map_by_spacer(no_dummy)
         self.assertEqual(no_dummy_out, true_out)
 
+    def test_subword_group_spacer_with_case_markup(self):
+        data_in = ['｟mrk_case_modifier_C｠', '▁however', ',', '▁according', '▁to', '▁the', '▁logs', ',', '▁｟mrk_begin_case_region_U｠', '▁she', '▁is', '▁hard', '-', 'working', '.', '▁｟mrk_end_case_region_U｠']  # noqa: E501
+        true_out = [0, 0, 0, 1, 2, 3, 4, 4, 5, 5, 6, 7, 7, 7, 7, 7]
+        out = subword_map_by_spacer(data_in)
+        self.assertEqual(out, true_out)
+
+    def test_subword_group_spacer_with_spacer_new(self):
+        data_in = ['｟mrk_case_modifier_C｠', '▁', 'however', ',', '▁', 'according', '▁', 'to', '▁', 'the', '▁', 'logs', ',', '▁', '｟mrk_begin_case_region_U｠', '▁', 'she', '▁', 'is', '▁', 'hard', '-', 'working', '.', '▁', '｟mrk_end_case_region_U｠']  # noqa: E501
+        true_out = [0, 0, 0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 4, 5, 5, 5, 5, 6, 6, 7, 7, 7, 7, 7, 7, 7]
+        out = subword_map_by_spacer(data_in)
+        self.assertEqual(out, true_out)
+
 
 if __name__ == '__main__':
     unittest.main()
diff --git a/onmt/tests/test_text_dataset.py b/onmt/tests/test_text_dataset.py
index e4d22e9c0a..4477bca7fe 100644
--- a/onmt/tests/test_text_dataset.py
+++ b/onmt/tests/test_text_dataset.py
@@ -79,7 +79,8 @@ def test_preprocess_shape(self):
                 self.INIT_CASES, self.PARAMS):
             init_case = self.initialize_case(init_case, params)
             mf = TextMultiField(**init_case)
-            sample_str = "dummy input here ."
+
+            sample_str = {"base_field": "dummy input here .", "a": "A A B D", "r": "C C C C", "b": "D F E D", "zbase_field": "another dummy input ."}
             proc = mf.preprocess(sample_str)
             self.assertEqual(len(proc), len(init_case["feats_fields"]) + 1)
 
@@ -147,7 +148,7 @@ def test_read(self):
         ]
         rdr = TextDataReader()
         for i, ex in enumerate(rdr.read(strings, "src")):
-            self.assertEqual(ex["src"], strings[i].decode("utf-8"))
+            self.assertEqual(ex["src"], {"src": strings[i].decode("utf-8")})
 
 
 class TestTextDataReaderFromFS(unittest.TestCase):
@@ -174,4 +175,23 @@ def tearDownClass(cls):
     def test_read(self):
         rdr = TextDataReader()
         for i, ex in enumerate(rdr.read(self.FILE_NAME, "src")):
-            self.assertEqual(ex["src"], self.STRINGS[i].decode("utf-8"))
+            self.assertEqual(ex["src"], {"src": self.STRINGS[i].decode("utf-8")})
+
+class TestTextDataReaderWithFeatures(unittest.TestCase):
+    def test_read(self):
+        strings = [
+            "hello world".encode("utf-8"),
+            "this's a string with punctuation .".encode("utf-8"),
+            "ThIs Is A sTrInG wItH oDD CapitALIZAtion".encode("utf-8")
+        ]
+        features = {
+            "feat_0": [
+                "A A".encode("utf-8"),
+                "A A B B C".encode("utf-8"),
+                "A A D D E E".encode("utf-8")
+                ]
+            }
+    
+        rdr = TextDataReader()
+        for i, ex in enumerate(rdr.read(strings, "src", features)):
+            self.assertEqual(ex["src"], {"src": strings[i].decode("utf-8"), "feat_0": features["feat_0"][i].decode("utf-8")})
\ No newline at end of file
diff --git a/onmt/tests/test_transform.py b/onmt/tests/test_transform.py
index 4bfa8be3bc..d99bc607de 100644
--- a/onmt/tests/test_transform.py
+++ b/onmt/tests/test_transform.py
@@ -509,3 +509,25 @@ def test_span_infilling(self):
         # n_masked = math.ceil(n_words * bart_noise.mask_ratio)
         # print(f"Text Span Infilling: {infillied} / {tokens}")
         # print(n_words, n_masked)
+
+class TestFeaturesTransform(unittest.TestCase):
+    def test_inferfeats(self):
+        inferfeats_cls = get_transforms_cls(["inferfeats"])["inferfeats"]
+        opt = Namespace(reversible_tokenization="joiner")
+        inferfeats_transform = inferfeats_cls(opt)
+
+        ex_in = {
+            "src": ['however', '￭,', 'according', 'to', 'the', 'logs', '￭,', 'she', 'is', 'hard', '￭-￭', 'working', '￭.'],
+            "tgt": ['however', '￭,', 'according', 'to', 'the', 'logs', '￭,', 'she', 'is', 'hard', '￭-￭', 'working', '￭.']
+        }
+        ex_out = inferfeats_transform.apply(ex_in)
+        self.assertIs(ex_out, ex_in)
+
+        ex_in["src_feats"] = {"feat_0": ["A", "A", "A", "A", "B", "A", "A", "C"]}
+        ex_out = inferfeats_transform.apply(ex_in)
+        self.assertEqual(ex_out["src_feats"]["feat_0"], ["A", "<null>", "A", "A", "A", "B", "<null>", "A", "A", "C", "<null>", "C", "<null>"])
+
+        ex_in["src"] = ['｟mrk_case_modifier_C｠', 'however', '￭,', 'according', 'to', 'the', 'logs', '￭,', '｟mrk_begin_case_region_U｠', 'she', 'is', 'hard', '￭-￭', 'working', '￭.', '｟mrk_end_case_region_U｠']
+        ex_in["src_feats"] = {"feat_0": ["A", "A", "A", "A", "B", "A", "A", "C"]}
+        ex_out = inferfeats_transform.apply(ex_in)
+        self.assertEqual(ex_out["src_feats"]["feat_0"], ["<null>", "A", "<null>", "A", "A", "A", "B", "<null>", "<null>", "A", "A", "C", "<null>", "C", "<null>", "<null>"])
diff --git a/onmt/transforms/features.py b/onmt/transforms/features.py
new file mode 100644
index 0000000000..24f02e30fe
--- /dev/null
+++ b/onmt/transforms/features.py
@@ -0,0 +1,90 @@
+from onmt.utils.logging import logger
+from onmt.transforms import register_transform
+from .transform import Transform, ObservableStats
+from onmt.constants import DefaultTokens, SubwordMarker
+from onmt.utils.alignment import subword_map_by_joiner, subword_map_by_spacer
+import re
+from collections import defaultdict
+
+
+@register_transform(name='filterfeats')
+class FilterFeatsTransform(Transform):
+    """Filter out examples with a mismatch between source and features."""
+
+    def __init__(self, opts):
+        super().__init__(opts)
+
+    @classmethod
+    def add_options(cls, parser):
+        pass
+
+    def _parse_opts(self):
+        pass
+
+    def apply(self, example, is_train=False, stats=None, **kwargs):
+        """Return None if mismatch"""
+
+        if 'src_feats' not in example:
+            # Do nothing
+            return example
+
+        for feat_name, feat_values in example['src_feats'].items():
+            if len(example['src']) != len(feat_values):
+                logger.warning(f"Skipping example due to mismatch between source and feature {feat_name}")
+                return None
+        return example
+
+    def _repr_args(self):
+        return ''
+
+
+@register_transform(name='inferfeats')
+class InferFeatsTransform(Transform):
+    """Infer features for subword tokenization."""
+
+    def __init__(self, opts):
+        super().__init__(opts)
+
+    @classmethod
+    def add_options(cls, parser):
+        """Avalilable options related to this Transform."""
+        group = parser.add_argument_group("Transform/InferFeats")
+        group.add("--reversible_tokenization", "-reversible_tokenization", default="joiner",
+                  choices=["joiner", "spacer"], help="Type of reversible tokenization applied on the tokenizer.")
+
+    def _parse_opts(self):
+        super()._parse_opts()
+        self.reversible_tokenization = self.opts.reversible_tokenization
+
+    def apply(self, example, is_train=False, stats=None, **kwargs):
+
+        if "src_feats" not in example:
+            # Do nothing
+            return example
+
+        if self.reversible_tokenization == "joiner":
+            word_to_subword_mapping = subword_map_by_joiner(example["src"])
+        else: #Spacer
+            word_to_subword_mapping = subword_map_by_spacer(example["src"])
+
+        inferred_feats = defaultdict(list)
+        for subword, word_id in zip(example["src"], word_to_subword_mapping):
+            for feat_name, feat_values in example["src_feats"].items():
+                # If case markup placeholder
+                if subword in SubwordMarker.CASE_MARKUP:
+                    inferred_feat = "<null>"
+                # Punctuation only (assumes joiner is also some punctuation token)
+                elif not re.sub(r'(\W)+', '', subword).strip():
+                    inferred_feat = "<null>"
+                else:
+                    inferred_feat = feat_values[word_id]
+
+                inferred_feats[feat_name].append(inferred_feat)
+
+        for feat_name, feat_values in inferred_feats.items():
+            example["src_feats"][feat_name] = inferred_feats[feat_name]
+
+        return example
+
+    def _repr_args(self):
+        return ''
\ No newline at end of file
diff --git a/onmt/translate/translator.py b/onmt/translate/translator.py
index 9bdd4ee4c7..4d37e982fa 100644
--- a/onmt/translate/translator.py
+++ b/onmt/translate/translator.py
@@ -333,6 +333,7 @@ def _gold_score(
     def translate(
         self,
         src,
+        src_feats={},
         tgt=None,
         batch_size=None,
         batch_type="sents",
@@ -345,6 +346,7 @@ def translate(
         Args:
             src: See :func:`self.src_reader.read()`.
             tgt: See :func:`self.tgt_reader.read()`.
+            src_feats: See :func`self.src_reader.read()`.
             batch_size (int): size of examples per mini-batch
             attn_debug (bool): enables the attention logging
             align_debug (bool): enables the word alignment logging
@@ -363,8 +365,8 @@ def translate(
         if self.tgt_prefix and tgt is None:
             raise ValueError("Prefix should be feed to tgt if -tgt_prefix.")
 
-        src_data = {"reader": self.src_reader, "data": src}
-        tgt_data = {"reader": self.tgt_reader, "data": tgt}
+        src_data = {"reader": self.src_reader, "data": src, "features": src_feats}
+        tgt_data = {"reader": self.tgt_reader, "data": tgt, "features": {}}
         _readers, _data = inputters.Dataset.config(
             [("src", src_data), ("tgt", tgt_data)]
         )
@@ -925,6 +927,7 @@ def _align_forward(self, batch, predictions):
     def translate(
         self,
         src,
+        src_feats={},
         tgt=None,
         batch_size=None,
         batch_type="sents",
@@ -945,6 +948,7 @@ def translate(
 
         return super(GeneratorLM, self).translate(
             src,
+            src_feats,
             tgt,
             batch_size=1,
             batch_type=batch_type,
diff --git a/onmt/utils/alignment.py b/onmt/utils/alignment.py
index 0a70edb33e..d775cf920c 100644
--- a/onmt/utils/alignment.py
+++ b/onmt/utils/alignment.py
@@ -120,25 +120,43 @@ def to_word_align(src, tgt, subword_align, m_src='joiner', m_tgt='joiner'):
     return " ".join(word_align)
 
 
-def subword_map_by_joiner(subwords, marker=SubwordMarker.JOINER):
+def subword_map_by_joiner(subwords, marker=SubwordMarker.JOINER, case_markup=SubwordMarker.CASE_MARKUP):
     """Return word id for each subword token (annotate by joiner)."""
-    flags = [0] * len(subwords)
+    flags = [1] * len(subwords)
     for i, tok in enumerate(subwords):
-        if tok.endswith(marker):
-            flags[i] = 1
-        if tok.startswith(marker):
-            assert i >= 1 and flags[i-1] != 1, \
+        if tok.endswith(marker) or (tok in case_markup and tok.find("end")<0):
+            flags[i] = 0
+        if tok.startswith(marker) or (tok in case_markup and tok.find("end")>=0):
+            assert i >= 1 and flags[i-1] != 0, \
                 "Sentence `{}` not correct!".format(" ".join(subwords))
-            flags[i-1] = 1
-    marker_acc = list(accumulate([0] + flags[:-1]))
-    word_group = [(i - maker_sofar) for i, maker_sofar
-                  in enumerate(marker_acc)]
+            flags[i-1] = 0
+    word_group = list(accumulate([0] + flags[:-1]))
     return word_group
 
 
-def subword_map_by_spacer(subwords, marker=SubwordMarker.SPACER):
+def subword_map_by_spacer(subwords, marker=SubwordMarker.SPACER, case_markup=SubwordMarker.CASE_MARKUP):
     """Return word id for each subword token (annotate by spacer)."""
-    word_group = list(accumulate([int(marker in x) for x in subwords]))
+    flags = [0] * len(subwords)
+    for i, tok in enumerate(subwords):
+        if marker in tok:
+            if tok.replace(marker, "") in case_markup:
+                if i < len(subwords)-1: 
+                    flags[i] = 1
+            else:
+                if i > 0:
+                    previous = subwords[i-1].replace(marker, "")
+                    if previous not in case_markup:
+                        flags[i] = 1
+
+    # In case there is a final case_markup when new_spacer is on
+    for i in range(1,len(subwords)-1):
+        if subwords[-i] in case_markup:
+            flags[-i] = 0
+        elif subwords[-i] == marker:
+            flags[-i] = 0
+            break
+
+    word_group = list(accumulate(flags))
     if word_group[0] == 1:  # when dummy prefix is set
         word_group = [item - 1 for item in word_group]
     return word_group
diff --git a/onmt/utils/parse.py b/onmt/utils/parse.py
index 2f4f1e1c45..4a12a5fe4d 100644
--- a/onmt/utils/parse.py
+++ b/onmt/utils/parse.py
@@ -75,6 +75,19 @@ def _validate_data(cls, opt):
                     logger.warning(f"Corpus {cname}'s weight should be given."
                                    " We default it to 1 for you.")
                 corpus['weight'] = 1
+
+            # Check features
+            src_feats = corpus.get("src_feats", None)
+            if src_feats is not None:
+                for feature_name, feature_file in src_feats.items():
+                    cls._validate_file(feature_file, info=f'{cname}/path_{feature_name}')
+                if 'inferfeats' not in corpus["transforms"]:
+                    raise ValueError(f"'inferfeats' transform is required when setting source features")
+                if 'filterfeats' not in corpus["transforms"]:
+                    raise ValueError(f"'filterfeats' transform is required when setting source features")
+            else:
+                corpus["src_feats"] = None
+
         logger.info(f"Parsed {len(corpora)} corpora from -data.")
         opt.data = corpora
 
@@ -107,6 +120,18 @@ def _get_all_transform(cls, opt):
     @classmethod
     def _validate_fields_opts(cls, opt, build_vocab_only=False):
         """Check options relate to vocab and fields."""
+
+        for cname, corpus in opt.data.items():
+            if cname != CorpusName.VALID and corpus["src_feats"] is not None:
+                    assert opt.src_feats_vocab, \
+                        "-src_feats_vocab is required if using source features."
+                    import yaml 
+                    opt.src_feats_vocab = yaml.safe_load(opt.src_feats_vocab)
+
+                    for feature in corpus["src_feats"].keys():
+                        assert feature in opt.src_feats_vocab, \
+                            f"No vocab file set for feature {feature}"
+
         if build_vocab_only:
             if not opt.share_vocab:
                 assert opt.tgt_vocab, \
@@ -295,4 +320,4 @@ def validate_train_opts(cls, opt):
 
     @classmethod
     def validate_translate_opts(cls, opt):
-        pass
+        opt.src_feats = eval(opt.src_feats) if opt.src_feats else {}