  Text matching is a fundamental problem in natural language processing. Neural
models using bidirectional LSTMs for sentence encoding and inter-sentence
attention mechanisms perform remarkably well on several benchmark datasets. We
propose REGMAPR - a simple and general architecture for text matching that does
not use inter-sentence attention. Starting from a Siamese architecture, we
augment the embeddings of the words with two features based on exact and para-
phrase match between words in the two sentences. We train the model using three
types of regularization on datasets for textual entailment, paraphrase
detection and semantic related- ness. REGMAPR performs comparably or better
than more complex neural models or models using a large number of handcrafted
features. REGMAPR achieves state-of-the-art results for paraphrase detection on
the SICK dataset and for textual entailment on the SNLI dataset among models
that do not use inter-sentence attention.
