Simultaneous Neural Machine Translation with Constituent Label Prediction
Simultaneous translation is a task in which translation begins before the speaker has finished speaking, so it is important to decide when to start the translation process. However, deciding whether to read more input words or start to translate is difficult for language pairs with different word orders such as English and Japanese. Motivated by the concept of pre-reordering, we propose a couple of simple decision rules using the label of the next constituent predicted by incremental constituent label prediction. In experiments on English-to-Japanese simultaneous translation, the proposed method outperformed baselines in the quality-latency trade-off.
Code (0)
등록된 구현이 없습니다.
Tasks
Machine TranslationPredictionTranslationSimilar Papers 제목 키워드 기반
Syntax-based Simultaneous Translation through Prediction of Unseen Syntactic Constituents
Encodings of Source Syntax: Similarities in NMT Representations Across Target Languages
We train neural machine translation (NMT) models from English to six target languages, using NMT encoder representations to predict ancestor constituent labels of source language words. We find that NMT encoders learn si…
Machine TranslationNMTTranslationLarge aligned treebanks for syntax-based machine translation
We present a collection of parallel treebanks that have been automatically aligned on both the terminal and the nonterminal constituent level for use in syntax-based machine translation. We describe how they were constru…
Language ModellingMachine TranslationTranslationPrediction Improves Simultaneous Neural Machine Translation
Simultaneous speech translation aims to maintain translation quality while minimizing the delay between reading input and incrementally producing the output. We propose a new general-purpose prediction action which predi…
Machine TranslationPredictionreinforcement-learningReinforcement Learning+2Improving Neural Machine Translation with Neural Syntactic Distance
The explicit use of syntactic information has been proved useful for neural machine translation (NMT). However, previous methods resort to either tree-structured neural networks or long linearized sequences, both of whic…
Machine TranslationNMTSentenceTranslation