Jing Zhang


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P-INT: A Path-based Interaction Model for Few-shot Knowledge Graph Completion
Jingwen Xu | Jing Zhang | Xirui Ke | Yuxiao Dong | Hong Chen | Cuiping Li | Yongbin Liu
Findings of the Association for Computational Linguistics: EMNLP 2021

Few-shot knowledge graph completion is to infer the unknown facts (i.e., query head-tail entity pairs) of a given relation with only a few observed reference entity pairs. Its general process is to first encode the implicit relation of an entity pair and then match the relation of a query entity pair with the relations of the reference entity pairs. Most existing methods have thus far encoded an entity pair and matched entity pairs by using the direct neighbors of concerned entities. In this paper, we propose the P-INT model for effective few-shot knowledge graph completion. First, P-INT infers and leverages the paths that can expressively encode the relation of two entities. Second, to capture the fine grained matches, P-INT calculates the interactions of paths instead of mix- ing them for each entity pair. Extensive experimental results demonstrate that P-INT out- performs the state-of-the-art baselines by 11.2– 14.2% in terms of Hits@1. Our codes and datasets are online now.

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A Pretraining Numerical Reasoning Model for Ordinal Constrained Question Answering on Knowledge Base
Yu Feng | Jing Zhang | Gaole He | Wayne Xin Zhao | Lemao Liu | Quan Liu | Cuiping Li | Hong Chen
Findings of the Association for Computational Linguistics: EMNLP 2021

Knowledge Base Question Answering (KBQA) is to answer natural language questions posed over knowledge bases (KBs). This paper targets at empowering the IR-based KBQA models with the ability of numerical reasoning for answering ordinal constrained questions. A major challenge is the lack of explicit annotations about numerical properties. To address this challenge, we propose a pretraining numerical reasoning model consisting of NumGNN and NumTransformer, guided by explicit self-supervision signals. The two modules are pretrained to encode the magnitude and ordinal properties of numbers respectively and can serve as model-agnostic plugins for any IR-based KBQA model to enhance its numerical reasoning ability. Extensive experiments on two KBQA benchmarks verify the effectiveness of our method to enhance the numerical reasoning ability for IR-based KBQA models.

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TA-MAMC at SemEval-2021 Task 4: Task-adaptive Pretraining and Multi-head Attention for Abstract Meaning Reading Comprehension
Jing Zhang | Yimeng Zhuang | Yinpei Su
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

This paper describes our system used in the SemEval-2021 Task4 Reading Comprehension of Abstract Meaning, achieving 1st for subtask 1 and 2nd for subtask 2 on the leaderboard. We propose an ensemble of ELECTRA-based models with task-adaptive pretraining and a multi-head attention multiple-choice classifier on top of the pre-trained model. The main contributions of our system are 1) revealing the performance discrepancy of different transformer-based pretraining models on the downstream task, 2) presentation of an efficient method to generate large task-adaptive corpora for pretraining. We also investigated several pretraining strategies and contrastive learning objectives. Our system achieves a test accuracy of 95.11 and 94.89 on subtask 1 and subtask 2 respectively.


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Research on attention memory networks as a model for learning natural language inference
Zhuang Liu | Degen Huang | Jing Zhang | Kaiyu Huang
Proceedings of the Workshop on Structured Prediction for NLP


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Rules-based Chinese Word Segmentation on MicroBlog for CIPS-SIGHAN on CLP2012
Jing Zhang | Degen Huang | Xia Han | Wei Wang
Proceedings of the Second CIPS-SIGHAN Joint Conference on Chinese Language Processing


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A Visualization method for machine translation evaluation results
Jian-Min Yao | Yun-Qian Qu | Qiao-Ming Zhu | Jing Zhang
Proceedings of the 20th Pacific Asia Conference on Language, Information and Computation

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Performance Analysis and Visualization of Machine Translation Evaluation
Jianmin Yao | Yunqian Qu | Qiang Lv | Qiaoming Zhu | Jing Zhang
International Journal of Computational Linguistics & Chinese Language Processing, Volume 11, Number 3, September 2006: Special Issue on Selected Papers from ROCLING XVII


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Speechalator: Two-Way Speech-to-Speech Translation in Your Hand
Alex Waibel | Ahmed Badran | Alan W. Black | Robert Frederking | Donna Gates | Alon Lavie | Lori Levin | Kevin Lenzo | Laura Mayfield Tomokiyo | Juergen Reichert | Tanja Schultz | Dorcas Wallace | Monika Woszczyna | Jing Zhang
Companion Volume of the Proceedings of HLT-NAACL 2003 - Demonstrations