Xiaomian Kang
2024
Self-Modifying State Modeling for Simultaneous Machine Translation
Donglei Yu
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Xiaomian Kang
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Yuchen Liu
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Yu Zhou
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Chengqing Zong
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Simultaneous Machine Translation (SiMT) generates target outputs while receiving stream source inputs and requires a read/write policy to decide whether to wait for the next source token or generate a new target token, whose decisions form a decision path. Existing SiMT methods, which learn the policy by exploring various decision paths in training, face inherent limitations. These methods not only fail to precisely optimize the policy due to the inability to accurately assess the individual impact of each decision on SiMT performance, but also cannot sufficiently explore all potential paths because of their vast number. Besides, building decision paths requires unidirectional encoders to simulate streaming source inputs, which impairs the translation quality of SiMT models. To solve these issues, we propose Self-Modifying State Modeling (SM2), a novel training paradigm for SiMT task. Without building decision paths, SM2 individually optimizes decisions at each state during training. To precisely optimize the policy, SM2 introduces Self-Modifying process to independently assess and adjust decisions at each state. For sufficient exploration, SM2 proposes Prefix Sampling to efficiently traverse all potential states. Moreover, SM2 ensures compatibility with bidirectional encoders, thus achieving higher translation quality. Experiments show that SM2 outperforms strong baselines. Furthermore, SM2 allows offline machine translation models to acquire SiMT ability with fine-tuning.
2020
Dynamic Context Selection for Document-level Neural Machine Translation via Reinforcement Learning
Xiaomian Kang
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Yang Zhao
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Jiajun Zhang
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Chengqing Zong
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Document-level neural machine translation has yielded attractive improvements. However, majority of existing methods roughly use all context sentences in a fixed scope. They neglect the fact that different source sentences need different sizes of context. To address this problem, we propose an effective approach to select dynamic context so that the document-level translation model can utilize the more useful selected context sentences to produce better translations. Specifically, we introduce a selection module that is independent of the translation module to score each candidate context sentence. Then, we propose two strategies to explicitly select a variable number of context sentences and feed them into the translation module. We train the two modules end-to-end via reinforcement learning. A novel reward is proposed to encourage the selection and utilization of dynamic context sentences. Experiments demonstrate that our approach can select adaptive context sentences for different source sentences, and significantly improves the performance of document-level translation methods.
2016
An End-to-End Chinese Discourse Parser with Adaptation to Explicit and Non-explicit Relation Recognition
Xiaomian Kang
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Haoran Li
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Long Zhou
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Jiajun Zhang
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Chengqing Zong
Proceedings of the CoNLL-16 shared task
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Co-authors
- Chengqing Zong 3
- Jiajun Zhang 2
- Donglei Yu 1
- Yuchen Liu 1
- Yu Zhou 1
- show all...