Bingquan Liu

Also published as: BingQuan Liu


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CN-HIT-IT.NLP at SemEval-2020 Task 4: Enhanced Language Representation with Multiple Knowledge Triples
Yice Zhang | Jiaxuan Lin | Yang Fan | Peng Jin | Yuanchao Liu | Bingquan Liu
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper describes our system that participated in the SemEval-2020 task 4: Commonsense Validation and Explanation. For this task, it is obvious that external knowledge, such as Knowledge graph, can help the model understand commonsense in natural language statements. But how to select the right triples for statements remains unsolved, so how to reduce the interference of irrelevant triples on model performance is a research focus. This paper adopt a modified K-BERT as the language encoder, to enhance language representation through triples from knowledge graphs. Experiments show that our method is better than models without external knowledge, and is slightly better than the original K-BERT. We got an accuracy score of 0.97 in subtaskA, ranking 1/45, and got an accuracy score of 0.948, ranking 2/35.


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Neural-based Chinese Idiom Recommendation for Enhancing Elegance in Essay Writing
Yuanchao Liu | Bo Pang | Bingquan Liu
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Although the proper use of idioms can enhance the elegance of writing, the active use of various expressions is a challenge because remembering idioms is difficult. In this study, we address the problem of idiom recommendation by leveraging a neural machine translation framework, in which we suppose that idioms are written with one pseudo target language. Two types of real-life datasets are collected to support this study. Experimental results show that the proposed approach achieves promising performance compared with other baseline methods.


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ITNLP-ARC at SemEval-2018 Task 12: Argument Reasoning Comprehension with Attention
Wenjie Liu | Chengjie Sun | Lei Lin | Bingquan Liu
Proceedings of The 12th International Workshop on Semantic Evaluation

Reasoning is a very important topic and has many important applications in the field of natural language processing. Semantic Evaluation (SemEval) 2018 Task 12 “The Argument Reasoning Comprehension” committed to research natural language reasoning. In this task, we proposed a novel argument reasoning comprehension system, ITNLP-ARC, which use Neural Networks technology to solve this problem. In our system, the LSTM model is involved to encode both the premise sentences and the warrant sentences. The attention model is used to merge the two premise sentence vectors. Through comparing the similarity between the attention vector and each of the two warrant vectors, we choose the one with higher similarity as our system’s final answer.

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LSDSCC: a Large Scale Domain-Specific Conversational Corpus for Response Generation with Diversity Oriented Evaluation Metrics
Zhen Xu | Nan Jiang | Bingquan Liu | Wenge Rong | Bowen Wu | Baoxun Wang | Zhuoran Wang | Xiaolong Wang
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

It has been proven that automatic conversational agents can be built up using the Endto-End Neural Response Generation (NRG) framework, and such a data-driven methodology requires a large number of dialog pairs for model training and reasonable evaluation metrics for testing. This paper proposes a Large Scale Domain-Specific Conversational Corpus (LSDSCC) composed of high-quality queryresponse pairs extracted from the domainspecific online forum, with thorough preprocessing and cleansing procedures. Also, a testing set, including multiple diverse responses annotated for each query, is constructed, and on this basis, the metrics for measuring the diversity of generated results are further presented. We evaluate the performances of neural dialog models with the widely applied diversity boosting strategies on the proposed dataset. The experimental results have shown that our proposed corpus can be taken as a new benchmark dataset for the NRG task, and the presented metrics are promising to guide the optimization of NRG models by quantifying the diversity of the generated responses reasonably.


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ITNLP-AiKF at SemEval-2017 Task 1: Rich Features Based SVR for Semantic Textual Similarity Computing
Wenjie Liu | Chengjie Sun | Lei Lin | Bingquan Liu
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

Semantic Textual Similarity (STS) devotes to measuring the degree of equivalence in the underlying semantic of the sentence pair. We proposed a new system, ITNLP-AiKF, which applies in the SemEval 2017 Task1 Semantic Textual Similarity track 5 English monolingual pairs. In our system, rich features are involved, including Ontology based, word embedding based, Corpus based, Alignment based and Literal based feature. We leveraged the features to predict sentence pair similarity by a Support Vector Regression (SVR) model. In the result, a Pearson Correlation of 0.8231 is achieved by our system, which is a competitive result in the contest of this track.

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Neural Response Generation via GAN with an Approximate Embedding Layer
Zhen Xu | Bingquan Liu | Baoxun Wang | Chengjie Sun | Xiaolong Wang | Zhuoran Wang | Chao Qi
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

This paper presents a Generative Adversarial Network (GAN) to model single-turn short-text conversations, which trains a sequence-to-sequence (Seq2Seq) network for response generation simultaneously with a discriminative classifier that measures the differences between human-produced responses and machine-generated ones. In addition, the proposed method introduces an approximate embedding layer to solve the non-differentiable problem caused by the sampling-based output decoding procedure in the Seq2Seq generative model. The GAN setup provides an effective way to avoid noninformative responses (a.k.a “safe responses”), which are frequently observed in traditional neural response generators. The experimental results show that the proposed approach significantly outperforms existing neural response generation models in diversity metrics, with slight increases in relevance scores as well, when evaluated on both a Mandarin corpus and an English corpus.


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WINGS:Writing with Intelligent Guidance and Suggestions
Xianjun Dai | Yuanchao Liu | Xiaolong Wang | Bingquan Liu
Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations


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Multimodal DBN for Predicting High-Quality Answers in cQA portals
Haifeng Hu | Bingquan Liu | Baoxun Wang | Ming Liu | Xiaolong Wang
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)


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Generating Questions from Web Community Contents
Baoxun Wang | Bingquan Liu | Chengjie Sun | Xiaolong Wang | Deyuan Zhang
Proceedings of COLING 2012: Demonstration Papers


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Learning to Detect Hedges and their Scope Using CRF
Qi Zhao | Chengjie Sun | Bingquan Liu | Yong Cheng
Proceedings of the Fourteenth Conference on Computational Natural Language Learning – Shared Task

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CRF tagging for head recognition based on Stanford parser
Yong Cheng | Chengjie Sun | Bingquan Liu | Lei Lin
CIPS-SIGHAN Joint Conference on Chinese Language Processing

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Modeling Semantic Relevance for Question-Answer Pairs in Web Social Communities
Baoxun Wang | Xiaolong Wang | Chengjie Sun | Bingquan Liu | Lin Sun
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics


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An Empirical Study of Non-Stationary Ngram Model and its Smoothing Techniques
Jinghui Xiao | Bingquan Liu | Xiaolong Wang
International Journal of Computational Linguistics & Chinese Language Processing, Volume 12, Number 2, June 2007

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Exploiting Pinyin Constraints in Pinyin-to-Character Conversion Task: a Class-Based Maximum Entropy Markov Model Approach
Jinghui Xiao | Bingquan Liu | Xiaolong Wang
International Journal of Computational Linguistics & Chinese Language Processing, Volume 12, Number 3, September 2007: Special Issue on Invited Papers from ISCSLP 2006


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Principles of Non-stationary Hidden Markov Model and Its Applications to Sequence Labeling Task
JingHui Xiao | BingQuan Liu | XiaoLong Wang
Second International Joint Conference on Natural Language Processing: Full Papers