Improved Sentence Modeling using Suffix Bidirectional LSTM

Understand

Recurrent neural networks have become ubiquitous in computing representations of sequential data, especially textual data in natural language processing.

• In particular, Bidirectional LSTMs are at the heart of several neural models achieving state-of-the-art performance in a wide variety of tasks in NLP.
• However, BiLSTMs are known to suffer from sequential bias - the contextual representation of a token is heavily influenced by tokens close to it in a sentence.
• We propose a general and effective improvement to the BiLSTM model which encodes each suffix and prefix of a sequence of tokens in both forward and reverse directions.