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+FACTORED NEURAL MACHINE TRANSLATION
+-----------------------------------
+
+Nematus supports arbitrary input features through factored representations, similar to factored models popularized with Moses.
+This can be used to add linguistic features such as lemmas, POS, or dependency labels, or potentially other types of information.
+The pipe symbol "|" serves as a factor separator and should not otherwise appear in the text.
+
+To use factored models, follow these steps:
+
+  - preprocess the source side of the training, development and test data to include factors. Consider this example sentence, in an unfactored (or 1-factored) representation, and with 4 factors per word:
+
+    Leonidas begged in the arena .
+
+    Leonidas|Leonidas|NNP|nsubj begged|beg|VBD|root in|in|IN|prep the|the|DT|det gladiatorial|gladiatorial|JJ|amod arena|arena|NN|pobj
+
+    https://github.com/rsennrich/wmt16-scripts/tree/master/factored_sample provides sample scripts to produce a factored representation from a CoNLL file, and BPE-segmented text.
+
+  - in the arguments to nematus.nmt.train, adjust the following options:
+    - factors: the number of factors per word
+    - dim_per_factor: the size of the embedding layer for each factor (a list of integers)
+    - dim_word: the total size of the input embedding (must match the sum of dim_per_factor)
+    - dictionaries: add a vocabulary file for each factor (in the order they appear), plus a vocabulary file for the target side
+
+    an example config is shown at https://github.com/rsennrich/wmt16-scripts/blob/master/factored_sample/config.py
+
+  - commands for training and running Nematus are otherwise identical to the non-factored version
+
+
+PUBLICATIONS
+------------
+
+factored neural machine translation is described in:
+
+Sennrich, Rico, Haddow, Barry (2016): Linguistic Input Features Improve Neural Machine Translation, Proc. of the First Conference on Machine Translation (WMT16). Berlin, Germany
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