The Chinese National Conference on Computational Linguistics (2021)


bib (full) Proceedings of the 20th Chinese National Conference on Computational Linguistics

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Proceedings of the 20th Chinese National Conference on Computational Linguistics
Sheng Li (李生) | Maosong Sun (孙茂松) | Yang Liu (刘洋) | Hua Wu (吴华) | Kang Liu (刘康) | Wanxiang Che (车万翔) | Shizhu He (何世柱) | Gaoqi Rao (饶高琦)

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Uyghur Metaphor Detection Via Considering Emotional ConsistencyUyghur Metaphor Detection Via Considering Emotional Consistency
Yang Qimeng | Yu Long | Tian Shengwei | Song Jinmiao

Metaphor detection plays an important role in tasks such as machine translation and human-machine dialogue. As more users express their opinions on products or other topics on socialmedia through metaphorical expressions this task is particularly especially topical. Most of the research in this field focuses on English and there are few studies on minority languages thatlack language resources and tools. Moreover metaphorical expressions have different meaningsin different language environments. We therefore established a deep neural network (DNN)framework for Uyghur metaphor detection tasks. The proposed method can focus on the multi-level semantic information of the text from word embedding part of speech and location which makes the feature representation more complete. We also use the emotional information of words to learn the emotional consistency features of metaphorical words and their context. A qualitative analysis further confirms the need for broader emotional information in metaphor detection. Ourresults indicate the performance of Uyghur metaphor detection can be effectively improved withthe help of multi-attention and emotional information.

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Multi-level Emotion Cause Analysis by Multi-head Attention Based Multi-task Learning
Li Xiangju | Feng Shi | Zhang Yifei | Wang Daling

Emotion cause analysis (ECA) aims to identify the potential causes behind certain emotions intext. Lots of ECA models have been designed to extract the emotion cause at the clause level. However in many scenarios only extracting the cause clause is ambiguous. To ease the problemin this paper we introduce multi-level emotion cause analysis which focuses on identifying emotion cause clause (ECC) and emotion cause keywords (ECK) simultaneously. ECK is a more challenging task since it not only requires capturing the specific understanding of the role of eachword in the clause but also the relation between each word and emotion expression. We observethat ECK task can incorporate the contextual information from the ECC task while ECC taskcan be improved by learning the correlation between emotion cause keywords and emotion fromthe ECK task. To fulfill the goal of joint learning we propose a multi-head attention basedmulti-task learning method which utilizes a series of mechanisms including shared and privatefeature extractor multi-head attention emotion attention and label embedding to capture featuresand correlations between the two tasks. Experimental results show that the proposed method consistently outperforms the state-of-the-art methods on a benchmark emotion cause dataset.

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Using Query Expansion in Manifold Ranking for Query-Oriented Multi-Document Summarization
Jia Quanye | Liu Rui | Lin Jianying

Manifold ranking has been successfully applied in query-oriented multi-document summariza-tion. It not only makes use of the relationships among the sentences but also the relationships between the given query and the sentences. However the information of original query is often insufficient. So we present a query expansion method which is combined in the manifold rank-ing to resolve this problem. Our method not only utilizes the information of the query term itselfand the knowledge base WordNet to expand it by synonyms but also uses the information of the document set itself to expand the query in various ways (mean expansion variance expansionand TextRank expansion). Compared with the previous query expansion methods our methodcombines multiple query expansion methods to better represent query information and at the same time it makes a useful attempt on manifold ranking. In addition we use the degree of wordoverlap and the proximity between words to calculate the similarity between sentences. We per-formed experiments on the datasets of DUC 2006 and DUC2007 and the evaluation results showthat the proposed query expansion method can significantly improve the system performance andmake our system comparable to the state-of-the-art systems.

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GCN with External Knowledge for Clinical Event DetectionGCN with External Knowledge for Clinical Event Detection
Liu Dan | Zhang Zhichang | Peng Hui | Han Ruirui

In recent years with the development of deep learning and the increasing demand for medical information acquisition in medical information technology applications such as clinical decision support Clinical Event Detection has been widely studied as its subtask. However directly applying advances in deep learning to Clinical Event Detection tasks often produces undesirable results. This paper proposes a multi-granularity information fusion encoder-decoder frameworkthat introduces external knowledge. First the word embedding generated by the pre-trained biomedical language representation model (BioBERT) and the character embedding generatedby the Convolutional Neural Network are spliced. And then perform Part-of-Speech attention coding for character-level embedding perform semantic Graph Convolutional Network codingfor the spliced character-word embedding. Finally the information of these three parts is fusedas Conditional Random Field input to generate the sequence label of the word. The experimental results on the 2012 i2b2 data set show that the model in this paper is superior to other existingmodels. In addition the model in this paper alleviates the problem that occurrence event typeseem more difficult to detect than other event types.

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A Prompt-independent and Interpretable Automated Essay Scoring Method for Chinese Second Language WritingChinese Second Language Writing
Wang Yupei | Hu Renfen

With the increasing popularity of learning Chinese as a second language (L2) the development of an automatic essay scoring (AES) method specially for Chinese L2 essays has become animportant task. To build a robust model that could easily adapt to prompt changes we propose 90linguistic features with consideration of both language complexity and correctness and introducethe Ordinal Logistic Regression model that explicitly combines these linguistic features and low-level textual representations. Our model obtains a high QWK of 0.714 a low RMSE of 1.516 anda considerable Pearson correlation of 0.734. With a simple linear model we further analyze the contribution of the linguistic features to score prediction revealing the model’s interpretability and its potential to give writing feedback to users. This work provides insights and establishes asolid baseline for Chinese L2 AES studies.