Enhancing Phrase-Based Statistical Machine Translation by Learning Phrase Representations Using Long Short-Term Memory Network
Benyamin Ahmadnia, Bonnie Jean Dorr · 2019
Phrases play a key role in Machine Translation (MT).In this paper, we apply a Long Short-Term Memory (LSTM) model over conventional Phrase-Based Statistical MT (PBSMT).The core idea is to use an LSTM encoder-decoder to score the phrase table generated by the PBSMT decoder.Given a source sequence, the encoder and decoder are jointly trained in order to maximize the conditional probability of a target sequence.Analytically, the performance of a PBSMT system is enhanced by using the conditional probabilities of phrase pairs computed by an LSTM encoder-decoder as an additional feature in the existing log-linear model.We compare the performance of the phrase tables in the PBSMT to the performance of the proposed LSTM and observe its positive impact on translation quality.We construct a PBSMT model using the Moses decoder and enrich the Language Model (LM) utilizing an external dataset.We then rank the phrase tables using an LSTM-based encoder-decoder.This method produces a gain of up to 3.14 BLEU score on the test set.