Sequence to Sequence Mixture Model for Diverse Machine Translation
Xuanli He, Gholamreza Haffari, Mohammad Norouzi · 2018
Sequence to sequence (SEQ2SEQ) models often lack diversity in their generated translations.This can be attributed to the limitation of SEQ2SEQ models in capturing lexical and syntactic variations in a parallel corpus resulting from different styles, genres, topics, or ambiguity of the translation process.In this paper, we develop a novel sequence to sequence mixture (S2SMIX) model that improves both translation diversity and quality by adopting a committee of specialized translation models rather than a single translation model.Each mixture component selects its own training dataset via optimization of the marginal loglikelihood, which leads to a soft clustering of the parallel corpus.Experiments on four language pairs demonstrate the superiority of our mixture model compared to a SEQ2SEQ baseline with standard or diversity-boosted beam search.Our mixture model uses negligible additional parameters and incurs no extra computation cost during decoding.