Na Ye


2025

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Reasoning Knowledge Filter for Logical Table-to-Text Generation
Yu Bai | Baoqiang Liu | Shuang Xue | Fang Cai | Na Ye | Guiping Zhang
Proceedings of Bridging Neurons and Symbols for Natural Language Processing and Knowledge Graphs Reasoning @ COLING 2025

Logical table-to-text generation (LT2T) seeks to produce logically faithful textual descriptions base on tables. Current end-to-end LT2T models, which use descriptions directly as learning objectives, frequently face challenges in maintaining logical faithfulness due to the lack of a reasoning knowledge. Recent research have introduced reasoning knowledge generated by models for LT2T task, but the noise along with it limited its performance. We therefore propose a framework reasoning knowledge filter that leverages the collaboration between large language models and smaller models to filter data points with high-quality reasoning knowledge. This framework aims to provide highly matched table, description and reasoning knowledge triplets for LT2T. The results obtained on LogicNLG database demonstrate that the efficiencies of the method in this paper has achieved optimal performance with a reduced amount of data. Specifically, it enhances SP-Acc by 1.4 points and NLI-Acc by 0.7 points compared to the current state-of-the-art model.

2016

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Interactive-Predictive Machine Translation based on Syntactic Constraints of Prefix
Na Ye | Guiping Zhang | Dongfeng Cai
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Interactive-predictive machine translation (IPMT) is a translation mode which combines machine translation technology and human behaviours. In the IPMT system, the utilization of the prefix greatly affects the interaction efficiency. However, state-of-the-art methods filter translation hypotheses mainly according to their matching results with the prefix on character level, and the advantage of the prefix is not fully developed. Focusing on this problem, this paper mines the deep constraints of prefix on syntactic level to improve the performance of IPMT systems. Two syntactic subtree matching rules based on phrase structure grammar are proposed to filter the translation hypotheses more strictly. Experimental results on LDC Chinese-English corpora show that the proposed method outperforms state-of-the-art phrase-based IPMT system while keeping comparable decoding speed.

2015

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Productivity promotion strategies for collaborative translation on huge volume technical documents
Guiping Zhang | Na Ye | Fang Cai | Chuang Wu | Xiangkui Sun | Jinfu Yuan | Dongfeng Cai
Proceedings of Machine Translation Summit XV: User Track

2011

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Study on the Impact Factors of the Translators’ Post-editing Efficiency in a Collaborative Translation Environment
Na Ye | Guiping Zhang
Proceedings of Machine Translation Summit XIII: Papers

2005

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Using Multiple Discriminant Analysis Approach for Linear Text Segmentation
Jingbo Zhu | Na Ye | Xinzhi Chang | Wenliang Chen | Benjamin K Tsou
Second International Joint Conference on Natural Language Processing: Full Papers