Jianfeng Li


Learning to Adapt to Low-Resource Paraphrase Generation
Zhigen Li | Yanmeng Wang | Rizhao Fan | Ye Wang | Jianfeng Li | Shaojun Wang
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Paraphrase generation is a longstanding NLP task and achieves great success with the aid of large corpora. However, transferring a paraphrasing model to another domain encounters the problem of domain shifting especially when the data is sparse. At the same time, widely using large pre-trained language models (PLMs) faces the overfitting problem when training on scarce labeled data. To mitigate these two issues, we propose, LAPA, an effective adapter for PLMs optimized by meta-learning. LAPA has three-stage training on three types of related resources to solve this problem: 1. pre-training PLMs on unsupervised corpora, 2. inserting an adapter layer and meta-training on source domain labeled data, and 3. fine-tuning adapters on a small amount of target domain labeled data. This method enables paraphrase generation models to learn basic language knowledge first, then learn the paraphrasing task itself later, and finally adapt to the target task. Our experimental results demonstrate that LAPA achieves state-of-the-art in supervised, unsupervised, and low-resource settings on three benchmark datasets. With only 2% of trainable parameters and 1% labeled data of the target task, our approach can achieve a competitive performance with previous work.


LSTM Neural Reordering Feature for Statistical Machine Translation
Yiming Cui | Shijin Wang | Jianfeng Li
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


The USTC machine translation system for IWSLT 2014
Shijin Wang | Yuguang Wang | Jianfeng Li | Yiming Cui | Lirong Dai
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign


The TCH machine translation system for IWSLT 2008.
Haifeng Wang | Hua Wu | Xiaoguang Hu | Zhanyi Liu | Jianfeng Li | Dengjun Ren | Zhengyu Niu
Proceedings of the 5th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper reports on the first participation of TCH (Toshiba (China) Research and Development Center) at the IWSLT evaluation campaign. We participated in all the 5 translation tasks with Chinese as source language or target language. For Chinese-English and English-Chinese translation, we used hybrid systems that combine rule-based machine translation (RBMT) method and statistical machine translation (SMT) method. For Chinese-Spanish translation, phrase-based SMT models were used. For the pivot task, we combined the translations generated by a pivot based statistical translation model and a statistical transfer translation model (firstly, translating from Chinese to English, and then from English to Spanish). Moreover, for better performance of MT, we improved each module in the MT systems as follows: adapting Chinese word segmentation to spoken language translation, selecting out-of-domain corpus to build language models, using bilingual dictionaries to correct word alignment results, handling NE translation and selecting translations from the outputs of multiple systems. According to the automatic evaluation results on the full test sets, we top in all the 5 tasks.


Discriminative Pruning of Language Models for Chinese Word Segmentation
Jianfeng Li | Haifeng Wang | Dengjun Ren | Guohua Li
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics


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Unsupervised Training for Overlapping Ambiguity Resolution in Chinese Word Segmentation
Mu Li | Jianfeng Gao | Chang-Ning Huang | Jianfeng Li
Proceedings of the Second SIGHAN Workshop on Chinese Language Processing