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Sentence pair modeling is critical for many NLP tasks, such as paraphraseidentification, semantic textual similarity, and natural language inference.Most state-of-the-art neural models for these tasks rely on pretrained wordembedding and compose sentence-level semantics in varied ways; however, fewworks have attempted to verify whether we really need pretrained embeddings inthese tasks. In this paper, we study how effective subword-level (character andcharacter n-gram) representations are in sentence pair modeling. Though it iswell-known that subword models are effective in tasks with single sentenceinput, including language modeling and machine translation, they have not beensystematically studied in sentence pair modeling tasks where the semantic andstring similarities between texts matter. Our experiments show that subwordmodels without any pretrained word embedding can achieve new state-of-the-artresults on two social media datasets and competitive results on news data forparaphrase identification.

Character-based Neural Networks for Sentence Pair Modeling