Widening the bottleneck of lexical choice for non-autoregressive translation

Abstract

Recently, non-autoregressive models have enjoyed great popularity in natural language processing (NLP) communities, and slowly crept into the main body of research such as speech recognition and computer vision. Non-autoregressive translation (NAT) has been proposed to improve the decoding efficiency of translation models by predicting all tokens independently and simultaneously. To reduce the complexity of the raw data, knowledge distillation (KD) is the preliminary step for training NAT models by leveraging autoregressive translation (AT). In this study, we first reveal that the discrepancy between the raw and the KD data leads to lexical choice errors on predicting low-frequency words. Then we bridge the gap by exploiting three architecture-free approaches without introducing any computational cost: (1) Model Level, where we introduce an extra Kullback–Leibler divergence term derived by comparing the lexical choice of NAT model and that embedded in the raw data; (2) Parallel Data Level, where we reactivate low-frequency information by proposing raw pre-training and reverse KD training; (3) Monolingual Data Level, where we transfer both the knowledge of the bilingual raw data and that of the new monolingual data to the NAT model. We conduct experiments on widely-used NAT benchmarks (i.e. WMT14 English–German and WMT16 Romanian–English) over two advanced NAT architectures. Results demonstrate that the proposed approaches can significantly and universally improve translation quality by reducing translation errors on low-frequency words. Extensive analyses demonstrate that (1) these approach generates translations that contain more low-frequency words; (2) these techniques can be used together profitably to further recall the useful information lost in the standard KD; (3) enlarging the monolingual data consistently improves the BLEU scores, while this trend does not hold when further scaling the monolingual data. To this end, we establish a new NAT benchmarks by validating our approaches on three additional datasets varying from languages and scales (i.e. WMT17 Chinese–English, WMT19 English–German and WAT17 Japanese–English). We will release data, code and models, which we hope can significantly promote research in this field.²