The University of Maryland’s Submissions to the WMT20 Chat Translation Task: Searching for More Data to Adapt Discourse-Aware Neural Machine Translation

Calvin Bao, Yow-Ting Shiue, Chujun Song, Jie Li, Marine Carpuat

Abstract
This paper describes the University of Maryland’s submissions to the WMT20 Shared Task on Chat Translation. We focus on translating agent-side utterances from English to German. We started from an off-the-shelf BPE-based standard transformer model trained with WMT17 news and fine-tuned it with the provided in-domain training data. In addition, we augment the training set with its best matches in the WMT19 news dataset. Our primary submission uses a standard Transformer, while our contrastive submissions use multi-encoder Transformers to attend to previous utterances. Our primary submission achieves 56.7 BLEU on the agent side (en→de), outperforming a baseline system provided by the task organizers by more than 13 BLEU points. Moreover, according to an evaluation on a set of carefully-designed examples, the multi-encoder architecture is able to generate more coherent translations.
Anthology ID:
2020.wmt-1.56
Volume:
Proceedings of the Fifth Conference on Machine Translation
Month:
November
Year:
2020
Address:
Online
Venue:
WMT
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
456–461
Language:
URL:
https://aclanthology.org/2020.wmt-1.56
DOI:
Bibkey:
Cite (ACL):
Calvin Bao, Yow-Ting Shiue, Chujun Song, Jie Li, and Marine Carpuat. 2020. The University of Maryland’s Submissions to the WMT20 Chat Translation Task: Searching for More Data to Adapt Discourse-Aware Neural Machine Translation. In Proceedings of the Fifth Conference on Machine Translation, pages 456–461, Online. Association for Computational Linguistics.
Cite (Informal):
The University of Maryland’s Submissions to the WMT20 Chat Translation Task: Searching for More Data to Adapt Discourse-Aware Neural Machine Translation (Bao et al., WMT 2020)
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