Neural Machine Translation with Extended Context

Peer reviewe

Improving Context-aware Neural Machine Translation with Target-side Context

In recent years, several studies on neural machine translation (NMT) have attempted to use document-level context by using a multi-encoder and two attention mechanisms to read the current and previous sentences to incorporate the context of the previous sentences. These studies concluded that the target-side context is less useful than the source-side context. However, we considered that the reason why the target-side context is less useful lies in the architecture used to model these contexts. Therefore, in this study, we investigate how the target-side context can improve context-aware neural machine translation. We propose a weight sharing method wherein NMT saves decoder states and calculates an attention vector using the saved states when translating a current sentence. Our experiments show that the target-side context is also useful if we plug it into NMT as the decoder state when translating a previous sentence.Comment: 12 pages; PACLING 201

Context-aware Neural Machine Translation for English-Japanese Business Scene Dialogues

Despite the remarkable advancements in machine translation, the current sentence-level paradigm faces challenges when dealing with highly-contextual languages like Japanese. In this paper, we explore how context-awareness can improve the performance of the current Neural Machine Translation (NMT) models for English-Japanese business dialogues translation, and what kind of context provides meaningful information to improve translation. As business dialogue involves complex discourse phenomena but offers scarce training resources, we adapted a pretrained mBART model, finetuning on multi-sentence dialogue data, which allows us to experiment with different contexts. We investigate the impact of larger context sizes and propose novel context tokens encoding extra-sentential information, such as speaker turn and scene type. We make use of Conditional Cross-Mutual Information (CXMI) to explore how much of the context the model uses and generalise CXMI to study the impact of the extra-sentential context. Overall, we find that models leverage both preceding sentences and extra-sentential context (with CXMI increasing with context size) and we provide a more focused analysis on honorifics translation. Regarding translation quality, increased source-side context paired with scene and speaker information improves the model performance compared to previous work and our context-agnostic baselines, measured in BLEU and COMET metrics.Comment: MT Summit 2023, research track, link to paper in proceedings: https://aclanthology.org/2023.mtsummit-research.23

Zunkobot：複数の知識モジュールを統合した雑談対話システム

会議名: 第84回 人工知能学会 言語・音声理解と対話処理研究会予稿のプレプリント

MT for Subtitling : Investigating professional translators’ user experience and feedback

This paper presents a study of machine translation and post-editing in the field of audiovisual translation. We analyse user experience data collected from post-editing tasks completed by twelve translators in four language pairs. We also present feedback provided by the translators in semi-structured interviews. The results of the user experience survey and thematic analysis of interviews shows that the translators’ impression of post-editing subtitles was on average neutral to somewhat negative, with segmentation and timing of subtitles identified as a key factor. Finally, we discuss the implications of the issues arising from the user experience survey and interviews for the future development of automatic subtitle translation