Papers with English-to-Japanese
Controlling Japanese Honorifics in English-to-Japanese Neural Machine Translation (D19-52)
Copied to clipboard
| Challenge: | In the Japanese language different levels of honorific speech are used to convey respect, deference, humility, formality and social distance. |
| Approach: | They propose a method for controlling the level of formality of Japanese output . they use heuristics to identify honorific verb forms to classify Japanese sentences . |
| Outcome: | The proposed model can produce Japanese translations in different honorific speech styles for the same English input sentence. |
NTT Neural Machine Translation Systems at WAT 2019 (D19-52)
Copied to clipboard
| Challenge: | We submitted two systems for scientific paper subtask and timely disclosure subtask . we evaluated the usefulness of incorporating external data from a wide variety of web pages to improve the translation quality. |
| Approach: | They describe two different translation tasks submitted to WAT 2019 . they submitted scientific paper subtasks and timely disclosure subtask . |
| Outcome: | The proposed system performed better on scientific paper and timely disclosure subtasks. |
Japanese Predicate Conjugation for Neural Machine Translation (N18-4)
Copied to clipboard
| Challenge: | Neural machine translation (NMT) has a drawback in that it can generate only high-frequency words owing to the computational costs of the softmax function in the output layer. |
| Approach: | They propose two methods to generate low-frequency words and deal with unknown words using Japanese predicate conjugation information without discarding linguistic information. |
| Outcome: | The proposed methods can generate low-frequency words and deal with unknown words. |
Neural Machine Translation Incorporating Named Entity (C18-1)
Copied to clipboard
| Challenge: | Conventional NMT models have difficulty translating words with multiple meanings because of the high ambiguity. |
| Approach: | They propose a neural machine translation model that incorporates named entity (NE) tags of source-language sentences to reduce the difficulty in translating multiple meanings. |
| Outcome: | The proposed model achieves 3.11 point improvement in bilingual evaluation understudy (BLEU) on English-to-Japanese translation task with the ASPEC, and English- to-Bulgarian and English to-Romanian translation tasks with the Europarl corpus. |
MELD-ST: An Emotion-aware Speech Translation Dataset (2024.findings-acl)
Copied to clipboard
Sirou Chen, Sakiko Yahata, Shuichiro Shimizu, Zhengdong Yang, Yihang Li, Chenhui Chu, Sadao Kurohashi
| Challenge: | Emotion plays a crucial role in human conversation. |
| Approach: | They present a MELD-ST dataset for the emotion-aware speech translation task . they show that fine-tuning with emotion labels can enhance translation performance . |
| Outcome: | The proposed dataset shows that fine tuning with emotion labels can improve translation performance in some settings. |
Statistical Analysis of Missing Translation in Simultaneous Interpretation Using A Large-scale Bilingual Speech Corpus (L18-1)
Copied to clipboard
| Challenge: | Various types of omissions have been described in simultaneous interpretation to improve interpretation quality or train interpreters. |
| Approach: | They analyze missing translations in simultaneous interpretations using a large-scale bilingual speech corpus. |
| Outcome: | The authors found that a high proportion of adverbs were missed in the translations . the authors suggest that omissions can be improved to improve interpretation quality . |