Part-of-Speech-Based Long Short-Term Memory Network for Learning Sentence Representations

Sentence representations play an important role in the field of natural language processing. While word representation has been applied to many natural language processing tasks, sentence representation has not been applied as widely due to the more complex structure and richer syntactic information of sentences. To learn sentence representations with structural and syntactic information, we propose a new model called the part-of-speech-based Long Short-Term Memory network (pos-LSTM) model. The pos-LSTM model generates a structural representation using the standard LSTM model and a syntactic representation using the part of speech; then, the pos-LSTM model obtains the final sentence representation by combining these two representations. The experimental results from 20 sentence similarity tasks and an entailment classification task show that the pos-LSTM model can better capture the syntactic information of sentences and generate higher quality sentence representations than traditional models.