Abstract
This paper presents IIGroup’s submission to the WMT22 Word-Level AutoCompletion(WLAC) Shared Task in four language directions. We propose to use a Generate-then-Rerank framework to solve this task. More specifically, the generator is used to generate candidate words and recall as many positive candidates as possible. To facilitate the training process of the generator, we propose a span-level mask prediction task. Once we get the candidate words, we take the top-K candidates and feed them into the reranker. The reranker is used to select the most confident candidate. The experimental results in four language directions demonstrate the effectiveness of our systems. Our systems achieve competitive performance ranking 1st in English to Chinese subtask and 2nd in Chinese to English subtask.- Anthology ID:
- 2022.wmt-1.121
- Volume:
- Proceedings of the Seventh Conference on Machine Translation (WMT)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Philipp Koehn, Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Tom Kocmi, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri, Aurélie Névéol, Mariana Neves, Martin Popel, Marco Turchi, Marcos Zampieri
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1187–1191
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.121
- DOI:
- Bibkey:
- Cite (ACL):
- Cheng Yang, Siheng Li, Chufan Shi, and Yujiu Yang. 2022. IIGROUP Submissions for WMT22 Word-Level AutoCompletion Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 1187–1191, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- IIGROUP Submissions for WMT22 Word-Level AutoCompletion Task (Yang et al., WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.121.pdf
Export citation
@inproceedings{yang-etal-2022-iigroup,
title = "{IIGROUP} Submissions for {WMT}22 Word-Level {A}uto{C}ompletion Task",
author = "Yang, Cheng and
Li, Siheng and
Shi, Chufan and
Yang, Yujiu",
editor = {Koehn, Philipp and
Barrault, Lo{\"\i}c and
Bojar, Ond{\v{r}}ej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss{\`a}, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Jimeno Yepes, Antonio and
Kocmi, Tom and
Martins, Andr{\'e} and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Popel, Martin and
Turchi, Marco and
Zampieri, Marcos},
booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wmt-1.121",
pages = "1187--1191",
abstract = "This paper presents IIGroup{'}s submission to the WMT22 Word-Level AutoCompletion(WLAC) Shared Task in four language directions. We propose to use a Generate-then-Rerank framework to solve this task. More specifically, the generator is used to generate candidate words and recall as many positive candidates as possible. To facilitate the training process of the generator, we propose a span-level mask prediction task. Once we get the candidate words, we take the top-K candidates and feed them into the reranker. The reranker is used to select the most confident candidate. The experimental results in four language directions demonstrate the effectiveness of our systems. Our systems achieve competitive performance ranking 1st in English to Chinese subtask and 2nd in Chinese to English subtask.",
}
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%0 Conference Proceedings %T IIGROUP Submissions for WMT22 Word-Level AutoCompletion Task %A Yang, Cheng %A Li, Siheng %A Shi, Chufan %A Yang, Yujiu %Y Koehn, Philipp %Y Barrault, Loïc %Y Bojar, Ondřej %Y Bougares, Fethi %Y Chatterjee, Rajen %Y Costa-jussà, Marta R. %Y Federmann, Christian %Y Fishel, Mark %Y Fraser, Alexander %Y Freitag, Markus %Y Graham, Yvette %Y Grundkiewicz, Roman %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Jimeno Yepes, Antonio %Y Kocmi, Tom %Y Martins, André %Y Morishita, Makoto %Y Monz, Christof %Y Nagata, Masaaki %Y Nakazawa, Toshiaki %Y Negri, Matteo %Y Névéol, Aurélie %Y Neves, Mariana %Y Popel, Martin %Y Turchi, Marco %Y Zampieri, Marcos %S Proceedings of the Seventh Conference on Machine Translation (WMT) %D 2022 %8 December %I Association for Computational Linguistics %C Abu Dhabi, United Arab Emirates (Hybrid) %F yang-etal-2022-iigroup %X This paper presents IIGroup’s submission to the WMT22 Word-Level AutoCompletion(WLAC) Shared Task in four language directions. We propose to use a Generate-then-Rerank framework to solve this task. More specifically, the generator is used to generate candidate words and recall as many positive candidates as possible. To facilitate the training process of the generator, we propose a span-level mask prediction task. Once we get the candidate words, we take the top-K candidates and feed them into the reranker. The reranker is used to select the most confident candidate. The experimental results in four language directions demonstrate the effectiveness of our systems. Our systems achieve competitive performance ranking 1st in English to Chinese subtask and 2nd in Chinese to English subtask. %U https://aclanthology.org/2022.wmt-1.121 %P 1187-1191
Markdown (Informal)
[IIGROUP Submissions for WMT22 Word-Level AutoCompletion Task](https://aclanthology.org/2022.wmt-1.121) (Yang et al., WMT 2022)
- IIGROUP Submissions for WMT22 Word-Level AutoCompletion Task (Yang et al., WMT 2022)
ACL
- Cheng Yang, Siheng Li, Chufan Shi, and Yujiu Yang. 2022. IIGROUP Submissions for WMT22 Word-Level AutoCompletion Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 1187–1191, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.