diff --git a/README.md b/README.md
index d2a5f0e6c1..e070bda2ad 100644
--- a/README.md
+++ b/README.md
@@ -74,8 +74,9 @@ The datasets module currently contains:
 - Sentiment analysis: SST and IMDb
 - Question classification: TREC
 - Entailment: SNLI
-- Language modeling: WikiText-2
-- Machine translation: Multi30k, IWSLT, WMT14
+- Language modeling: abstract class + WikiText-2
+- Machine translation: abstract class + Multi30k, IWSLT, WMT14
+- Sequence tagging (e.g. POS/NER): abstract class + UDPOS
 
 Others are planned or a work in progress:
 
diff --git a/test/sequence_tagging.py b/test/sequence_tagging.py
new file mode 100644
index 0000000000..eff0809288
--- /dev/null
+++ b/test/sequence_tagging.py
@@ -0,0 +1,47 @@
+from torchtext import data
+from torchtext import datasets
+
+# Define the fields associated with the sequences.
+WORD = data.Field(init_token="<bos>", eos_token="<eos>")
+UD_TAG = data.Field(init_token="<bos>", eos_token="<eos>")
+
+# Download and the load default data.
+train, val, test = datasets.UDPOS.splits(
+    fields=(('word', WORD), ('udtag', UD_TAG), (None, None)))
+
+print(train.fields)
+print(len(train))
+print(vars(train[0]))
+
+# We can also define more than two columns.
+WORD = data.Field(init_token="<bos>", eos_token="<eos>")
+UD_TAG = data.Field(init_token="<bos>", eos_token="<eos>")
+PTB_TAG = data.Field(init_token="<bos>", eos_token="<eos>")
+
+# Load the specified data.
+train, val, test = datasets.UDPOS.splits(
+    fields=(('word', WORD), ('udtag', UD_TAG), ('ptbtag', PTB_TAG)),
+    path=".data/sequence-labeling/en-ud-v2",
+    train="en-ud-tag.v2.train.txt",
+    validation="en-ud-tag.v2.dev.txt",
+    test="en-ud-tag.v2.test.txt")
+
+print(train.fields)
+print(len(train))
+print(vars(train[0]))
+
+WORD.build_vocab(train.word, min_freq=3)
+UD_TAG.build_vocab(train.udtag)
+PTB_TAG.build_vocab(train.ptbtag)
+
+print(UD_TAG.vocab.freqs)
+print(PTB_TAG.vocab.freqs)
+
+train_iter, val_iter = data.BucketIterator.splits(
+    (train, val), batch_size=3, device=0)
+
+batch = next(iter(train_iter))
+
+print("words", batch.word)
+print("udtags", batch.udtag)
+print("ptbtags", batch.ptbtag)
diff --git a/torchtext/datasets/__init__.py b/torchtext/datasets/__init__.py
index 43caad3f1e..c6541b79b5 100644
--- a/torchtext/datasets/__init__.py
+++ b/torchtext/datasets/__init__.py
@@ -2,6 +2,7 @@
 from .snli import SNLI
 from .sst import SST
 from .translation import TranslationDataset, Multi30k, IWSLT, WMT14  # NOQA
+from .sequence_tagging import SequenceTaggingDataset, UDPOS # NOQA
 from .trec import TREC
 from .imdb import IMDB
 
@@ -15,4 +16,6 @@
            'WMT14'
            'WikiText2',
            'TREC',
-           'IMDB']
+           'IMDB',
+           'SequenceTaggingDataset',
+           'UDPOS']
diff --git a/torchtext/datasets/sequence_tagging.py b/torchtext/datasets/sequence_tagging.py
new file mode 100644
index 0000000000..7f1ccef06f
--- /dev/null
+++ b/torchtext/datasets/sequence_tagging.py
@@ -0,0 +1,65 @@
+from .. import data
+
+
+class SequenceTaggingDataset(data.Dataset):
+    """Defines a dataset for sequence tagging. Examples in this dataset
+    contain paired lists -- paired list of words and tags.
+
+    For example, in the case of part-of-speech tagging, an example is of the
+    form
+    [I, love, PyTorch, .] paired with [PRON, VERB, PROPN, PUNCT]
+
+    See torchtext/test/sequence_tagging.py on how to use this class.
+    """
+
+    @staticmethod
+    def sort_key(example):
+        for attr in dir(example):
+            if not callable(getattr(example, attr)) and \
+                    not attr.startswith("__"):
+                return len(getattr(example, attr))
+        return 0
+
+    def __init__(self, path, fields, **kwargs):
+        examples = []
+        columns = []
+
+        with open(path) as input_file:
+            for line in input_file:
+                line = line.strip()
+                if line == "":
+                    if columns:
+                        examples.append(data.Example.fromlist(columns, fields))
+                    columns = []
+                else:
+                    for i, column in enumerate(line.split("\t")):
+                        if len(columns) < i + 1:
+                            columns.append([])
+                        columns[i].append(column)
+
+            if columns:
+                examples.append(data.Example.fromlist(columns, fields))
+        super(SequenceTaggingDataset, self).__init__(examples, fields,
+                                                     **kwargs)
+
+
+class UDPOS(SequenceTaggingDataset):
+
+    # Universal Dependencies English Web Treebank.
+    # Download original at http://universaldependencies.org/
+    # License: http://creativecommons.org/licenses/by-sa/4.0/
+    urls = ['https://bitbucket.org/sivareddyg/public/downloads/en-ud-v2.zip']
+    dirname = 'en-ud-v2'
+    name = 'udpos'
+
+    @classmethod
+    def splits(cls, fields, root=".data", train="en-ud-tag.v2.train.txt",
+               validation="en-ud-tag.v2.dev.txt",
+               test="en-ud-tag.v2.test.txt", **kwargs):
+        """Downloads and loads the Universal Dependencies Version 2 POS Tagged
+        data.
+        """
+
+        return super(UDPOS, cls).splits(
+            fields=fields, root=root, train=train, validation=validation,
+            test=test, **kwargs)