Zhongqing Wang


2022

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Discrete Opinion Tree Induction for Aspect-based Sentiment Analysis
Chenhua Chen | Zhiyang Teng | Zhongqing Wang | Yue Zhang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Dependency trees have been intensively used with graph neural networks for aspect-based sentiment classification. Though being effective, such methods rely on external dependency parsers, which can be unavailable for low-resource languages or perform worse in low-resource domains. In addition, dependency trees are also not optimized for aspect-based sentiment classification. In this paper, we propose an aspect-specific and language-agnostic discrete latent opinion tree model as an alternative structure to explicit dependency trees. To ease the learning of complicated structured latent variables, we build a connection between aspect-to-context attention scores and syntactic distances, inducing trees from the attention scores. Results on six English benchmarks and one Chinese dataset show that our model can achieve competitive performance and interpretability.

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Chinese Synesthesia Detection: New Dataset and Models
Xiaotong Jiang | Qingqing Zhao | Yunfei Long | Zhongqing Wang
Findings of the Association for Computational Linguistics: ACL 2022

In this paper, we introduce a new task called synesthesia detection, which aims to extract the sensory word of a sentence, and to predict the original and synesthetic sensory modalities of the corresponding sensory word. Synesthesia refers to the description of perceptions in one sensory modality through concepts from other modalities. It involves not only a linguistic phenomenon, but also a cognitive phenomenon structuring human thought and action, which makes it become a bridge between figurative linguistic phenomenon and abstract cognition, and thus be helpful to understand the deep semantics. To address this, we construct a large-scale human-annotated Chinese synesthesia dataset, which contains 7,217 annotated sentences accompanied by 187 sensory words. Based on this dataset, we propose a family of strong and representative baseline models. Upon these baselines, we further propose a radical-based neural network model to identify the boundary of the sensory word, and to jointly detect the original and synesthetic sensory modalities for the word. Through extensive experiments, we observe that the importance of the proposed task and dataset can be verified by the statistics and progressive performances. In addition, our proposed model achieves state-of-the-art results on the synesthesia dataset.

2020

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Sentiment Forecasting in Dialog
Zhongqing Wang | Xiujun Zhu | Yue Zhang | Shoushan Li | Guodong Zhou
Proceedings of the 28th International Conference on Computational Linguistics

Sentiment forecasting in dialog aims to predict the polarity of next utterance to come, and can help speakers revise their utterances in sentimental utterances generation. However, the polarity of next utterance is normally hard to predict, due to the lack of content of next utterance (yet to come). In this study, we propose a Neural Sentiment Forecasting (NSF) model to address inherent challenges. In particular, we employ a neural simulation model to simulate the next utterance based on the context (previous utterances encountered). Moreover, we employ a sequence influence model to learn both pair-wise and seq-wise influence. Empirical studies illustrate the importance of proposed sentiment forecasting task, and justify the effectiveness of our NSF model over several strong baselines.

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Multi-Turn Dialogue Generation in E-Commerce Platform with the Context of Historical Dialogue
WeiSheng Zhang | Kaisong Song | Yangyang Kang | Zhongqing Wang | Changlong Sun | Xiaozhong Liu | Shoushan Li | Min Zhang | Luo Si
Findings of the Association for Computational Linguistics: EMNLP 2020

As an important research topic, customer service dialogue generation tends to generate generic seller responses by leveraging current dialogue information. In this study, we propose a novel and extensible dialogue generation method by leveraging sellers’ historical dialogue information, which can be both accessible and informative. By utilizing innovative historical dialogue representation learning and historical dialogue selection mechanism, the proposed model is capable of detecting most related responses from sellers’ historical dialogues, which can further enhance the current dialogue generation quality. Unlike prior dialogue generation efforts, we treat each seller’s historical dialogues as a list of Customer-Seller utterance pairs and allow the model to measure their different importance, and copy words directly from most relevant pairs. Extensive experimental results show that the proposed approach can generate high-quality responses that cater to specific sellers’ characteristics and exhibit consistent superiority over baselines on a real-world multi-turn customer service dialogue dataset.

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基于对话约束的回复生成研究(Research on Response Generation via Dialogue Constraints)
Mengyu Guan (管梦雨) | Zhongqing Wang (王中卿) | Shoushan Li (李寿山) | Guodong Zhou (周国栋)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

现有的对话系统中存在着生成“好的”、“我不知道”等无意义的安全回复问题。日常对话中,对话者通常围绕特定的主题进行讨论且每句话都有明显的情感和意图。因此该文提出了基于对话约束的回复生成模型,即在Seq2Seq模型的基础上,结合对对话的主题、情感、意图的识别。该方法对生成回复的主题、情感和意图进行约束,从而生成具有合理的情感和意图且与对话主题相关的回复。实验证明,该文提出的方法能有效地提高生成回复的质量。

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基于阅读理解框架的中文事件论元抽取(Chinese Event Argument Extraction using Reading Comprehension Framework)
Min Chen (陈敏) | Fan Wu (吴凡) | Zhongqing Wang (王中卿) | Peifeng Li (李培峰) | Qiaoming Zhu (朱巧明)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

传统的事件论元抽取方法把该任务当作句子中实体提及的多分类或序列标注任务,论元角色的类别在这些方法中只能作为向量表示,而忽略了论元角色的先验信息。实际上,论元角色的语义和论元本身有很大关系。对此,本文提议将其当作机器阅读理解任务,把论元角色表述为自然语言描述的问题,通过在上下文中回答这些问题来抽取论元。该方法更好地利用了论元角色类别的先验信息,在ACE2005中文语料上的实验证明了该方法的有效性。

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基于半监督学习的中文社交文本事件聚类方法(Semi-supervised Method to Cluster Chinese Events on Social Streams)
Hengrui Guo (郭恒睿) | Zhongqing Wang (王中卿) | Peifeng Li (李培峰) | Qiaoming Zhu (朱巧明)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

面向社交媒体的事件聚类旨在根据事件特征对短文本聚类。目前,事件聚类模型主要分为无监督模型和有监督模型。无监督模型聚类效果较差,有监督模型依赖大量标注数据。基于此,本文提出了一种半监督事件聚类模型(SemiEC),该模型在小规模标注数据的基础上,利用LSTM表征事件,利用线性模型计算文本相似度,进行增量聚类,利用增量聚类产生的标注数据对模型再训练,结束后对不确定样本再聚类。实验表明,SemiEC的性能相比其他模型均有所提高。

2019

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Emotion Detection with Neural Personal Discrimination
Xiabing Zhou | Zhongqing Wang | Shoushan Li | Guodong Zhou | Min Zhang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

There have been a recent line of works to automatically predict the emotions of posts in social media. Existing approaches consider the posts individually and predict their emotions independently. Different from previous researches, we explore the dependence among relevant posts via the authors’ backgrounds, since the authors with similar backgrounds, e.g., gender, location, tend to express similar emotions. However, such personal attributes are not easy to obtain in most social media websites, and it is hard to capture attributes-aware words to connect similar people. Accordingly, we propose a Neural Personal Discrimination (NPD) approach to address above challenges by determining personal attributes from posts, and connecting relevant posts with similar attributes to jointly learn their emotions. In particular, we employ adversarial discriminators to determine the personal attributes, with attention mechanisms to aggregate attributes-aware words. In this way, social correlationship among different posts can be better addressed. Experimental results show the usefulness of personal attributes, and the effectiveness of our proposed NPD approach in capturing such personal attributes with significant gains over the state-of-the-art models.

2018

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Questions as a Pre-event, Pivot Event and Post-event of Emotions
Helena Yan Ping Lau | Sophia Yat Mei Lee | Zhongqing Wang
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation

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Stance Detection with Hierarchical Attention Network
Qingying Sun | Zhongqing Wang | Qiaoming Zhu | Guodong Zhou
Proceedings of the 27th International Conference on Computational Linguistics

Stance detection aims to assign a stance label (for or against) to a post toward a specific target. Recently, there is a growing interest in using neural models to detect stance of documents. Most of these works model the sequence of words to learn document representation. However, much linguistic information, such as polarity and arguments of the document, is correlated with the stance of the document, and can inspire us to explore the stance. Hence, we present a neural model to fully employ various linguistic information to construct the document representation. In addition, since the influences of different linguistic information are different, we propose a hierarchical attention network to weigh the importance of various linguistic information, and learn the mutual attention between the document and the linguistic information. The experimental results on two datasets demonstrate the effectiveness of the proposed hierarchical attention neural model.

2017

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Integrating Order Information and Event Relation for Script Event Prediction
Zhongqing Wang | Yue Zhang | Ching-Yun Chang
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

There has been a recent line of work automatically learning scripts from unstructured texts, by modeling narrative event chains. While the dominant approach group events using event pair relations, LSTMs have been used to encode full chains of narrative events. The latter has the advantage of learning long-range temporal orders, yet the former is more adaptive to partial orders. We propose a neural model that leverages the advantages of both methods, by using LSTM hidden states as features for event pair modelling. A dynamic memory network is utilized to automatically induce weights on existing events for inferring a subsequent event. Standard evaluation shows that our method significantly outperforms both methods above, giving the best results reported so far.

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Opinion Recommendation Using A Neural Model
Zhongqing Wang | Yue Zhang
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We present opinion recommendation, a novel task of jointly generating a review with a rating score that a certain user would give to a certain product which is unreviewed by the user, given existing reviews to the product by other users, and the reviews that the user has given to other products. A characteristic of opinion recommendation is the reliance of multiple data sources for multi-task joint learning. We use a single neural network to model users and products, generating customised product representations using a deep memory network, from which customised ratings and reviews are constructed jointly. Results show that our opinion recommendation system gives ratings that are closer to real user ratings on Yelp.com data compared with Yelp’s own ratings. our methods give better results compared to several pipelines baselines.

2016

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A Bilingual Attention Network for Code-switched Emotion Prediction
Zhongqing Wang | Yue Zhang | Sophia Lee | Shoushan Li | Guodong Zhou
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Emotions in code-switching text can be expressed in either monolingual or bilingual forms. However, relatively little research has emphasized on code-switching text. In this paper, we propose a Bilingual Attention Network (BAN) model to aggregate the monolingual and bilingual informative words to form vectors from the document representation, and integrate the attention vectors to predict the emotion. The experiments show that the effectiveness of the proposed model. Visualization of the attention layers illustrates that the model selects qualitatively informative words.

2015

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Emotion Detection in Code-switching Texts via Bilingual and Sentimental Information
Zhongqing Wang | Sophia Lee | Shoushan Li | Guodong Zhou
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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Emotion in Code-switching Texts: Corpus Construction and Analysis
Sophia Lee | Zhongqing Wang
Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing

2014

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Skill Inference with Personal and Skill Connections
Zhongqing Wang | Shoushan Li | Hanxiao Shi | Guodong Zhou
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2013

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Collective Personal Profile Summarization with Social Networks
Zhongqing Wang | Shoushan Li | Fang Kong | Guodong Zhou
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

2012

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Soochow University Word Segmenter for SIGHAN 2012 Bakeoff
Yan Fang | Zhongqing Wang | Shoushan Li | Zhongguo Li | Richen Xu | Leixin Cai
Proceedings of the Second CIPS-SIGHAN Joint Conference on Chinese Language Processing