Mingming Yang


2023

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Rethinking Word-Level Auto-Completion in Computer-Aided Translation
Xingyu Chen | Lemao Liu | Guoping Huang | Zhirui Zhang | Mingming Yang | Shuming Shi | Rui Wang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Word-level auto-completion (WLAC) plays a crucial role in Computer-Assisted Translation. While previous studies have primarily focused on designing complex model architectures, this paper takes a different perspective by rethinking the fundamental question: what kind of words are good auto-completions? We introduce a measurable criterion to address this question and discover that existing WLAC models often fail to meet this criterion. Building upon this observation, we propose an effective approach to enhance WLAC performance by promoting adherence to the criterion. Notably, the proposed approach is general and can be applied to various encoder-based architectures. Through extensive experiments, we demonstrate that our approach outperforms the top-performing system submitted to the WLAC shared tasks in WMT2022, while utilizing significantly smaller model sizes.

2019

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Sentence-Level Agreement for Neural Machine Translation
Mingming Yang | Rui Wang | Kehai Chen | Masao Utiyama | Eiichiro Sumita | Min Zhang | Tiejun Zhao
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

The training objective of neural machine translation (NMT) is to minimize the loss between the words in the translated sentences and those in the references. In NMT, there is a natural correspondence between the source sentence and the target sentence. However, this relationship has only been represented using the entire neural network and the training objective is computed in word-level. In this paper, we propose a sentence-level agreement module to directly minimize the difference between the representation of source and target sentence. The proposed agreement module can be integrated into NMT as an additional training objective function and can also be used to enhance the representation of the source sentences. Empirical results on the NIST Chinese-to-English and WMT English-to-German tasks show the proposed agreement module can significantly improve the NMT performance.