Context-aware Neural Machine Translation with Coreference Information
We present neural machine translation models for translating a sentence in a text by using a graph-based encoder which can consider coreference relations provided within the text explicitly. The graph-based encoder can dynamically encode the source text without attending to all tokens in the text. In experiments, our proposed models provide statistically significant improvement to the previous approach of at most 0.9 points in the BLEU score on the OpenSubtitle2018 English-to-Japanese data set. Experimental results also show that the graph-based encoder can handle a longer text well, compared with the previous approach.
Code (0)
등록된 구현이 없습니다.
Tasks
Machine TranslationSentenceTranslationSimilar Papers 제목 키워드 기반
Contrastive Learning for Context-aware Neural Machine TranslationUsing Coreference Information
Context-aware neural machine translation (NMT) incorporates contextual information of surrounding texts, that can improve the translation quality of document-level machine translation. Many existing works on context-awar…
Contrastive Learningcoreference-resolutionCoreference ResolutionData Augmentation+5Contrastive Learning for Context-aware Neural Machine Translation Using Coreference Information
Context-aware neural machine translation (NMT) incorporates contextual information of surrounding texts, that can improve the translation quality of document-level machine translation. Many existing works on context-awar…
Contrastive Learningcoreference-resolutionCoreference ResolutionData Augmentation+5Coreference and Coherence in Neural Machine Translation: A Study Using Oracle Experiments
Cross-sentence context can provide valuable information in Machine Translation and is critical for translation of anaphoric pronouns and for providing consistent translations. In this paper, we devise simple oracle exper…
Coreference ResolutionLanguage ModelingLanguage ModellingMachine Translation+3Context-Aware Machine Translation with Source Coreference Explanation
Despite significant improvements in enhancing the quality of translation, context-aware machine translation (MT) models underperform in many cases. One of the main reasons is that they fail to utilize the correct feature…
Machine TranslationTranslationContext-Aware Neural Machine Translation Learns Anaphora Resolution
Standard machine translation systems process sentences in isolation and hence ignore extra-sentential information, even though extended context can both prevent mistakes in ambiguous cases and improve translation coheren…
Machine TranslationTranslation