Chunliang Zhang


2023

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Augmenting Large Language Model Translators via Translation Memories
Yongyu Mu | Abudurexiti Reheman | Zhiquan Cao | Yuchun Fan | Bei Li | Yinqiao Li | Tong Xiao | Chunliang Zhang | Jingbo Zhu
Findings of the Association for Computational Linguistics: ACL 2023

Using translation memories (TMs) as prompts is a promising approach to in-context learning of machine translation models. In this work, we take a step towards prompting large language models (LLMs) with TMs and making them better translators. We find that the ability of LLMs to “understand” prompts is indeed helpful for making better use of TMs. Experiments show that the results of a pre-trained LLM translator can be greatly improved by using high-quality TM-based prompts. These results are even comparable to those of the state-of-the-art NMT systems which have access to large-scale in-domain bilingual data and are well tuned on the downstream tasks.

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Rethinking and Improving Multi-task Learning for End-to-end Speech Translation
Yuhao Zhang | Chen Xu | Bei Li | Hao Chen | Tong Xiao | Chunliang Zhang | Jingbo Zhu
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Significant improvements in end-to-end speech translation (ST) have been achieved through the application of multi-task learning. However, the extent to which auxiliary tasks are highly consistent with the ST task, and how much this approach truly helps, have not been thoroughly studied. In this paper, we investigate the consistency between different tasks, considering different times and modules. We find that the textual encoder primarily facilitates cross-modal conversion, but the presence of noise in speech impedes the consistency between text and speech representations. Furthermore, we propose an improved multi-task learning (IMTL) approach for the ST task, which bridges the modal gap by mitigating the difference in length and representation. We conduct experiments on the MuST-C dataset. The results demonstrate that our method attains state-of-the-art results. Moreover, when additional data is used, we achieve the new SOTA result on MuST-C English to Spanish task with 20.8% of the training time required by the current SOTA method.

2019

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Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition
Yufan Jiang | Chi Hu | Tong Xiao | Chunliang Zhang | Jingbo Zhu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

In this paper, we study differentiable neural architecture search (NAS) methods for natural language processing. In particular, we improve differentiable architecture search by removing the softmax-local constraint. Also, we apply differentiable NAS to named entity recognition (NER). It is the first time that differentiable NAS methods are adopted in NLP tasks other than language modeling. On both the PTB language modeling and CoNLL-2003 English NER data, our method outperforms strong baselines. It achieves a new state-of-the-art on the NER task.

2014

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A Hybrid Approach to Skeleton-based Translation
Tong Xiao | Jingbo Zhu | Chunliang Zhang
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2012

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Learning Better Rule Extraction with Translation Span Alignment
Jingbo Zhu | Tong Xiao | Chunliang Zhang
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2011

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Unsupervised Discovery of Domain-Specific Knowledge from Text
Dirk Hovy | Chunliang Zhang | Eduard Hovy | Anselmo Peñas
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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An Empirical Evaluation of Data-Driven Paraphrase Generation Techniques
Donald Metzler | Eduard Hovy | Chunliang Zhang
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies