pwc-archive/papers-with-abstracts huggingface dataset:
多目标情感分类中文数据集构建及分析研究(Construction and Analysis of Chinese Multi-Target Sentiment Classification Dataset)
Authors: Pengyuan Liu, Yongsheng Tian, Chengyu Du, Likun Qiu
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10
Tasks: Sentiment Analysis, Sentiment Classification

目标级情感分类任务是要得到句子中特定评价目标的情感倾向。一个评论句中往往存在多个目标,多个目标的情感可能一致,也可能不一致。但在已有针对目标级情感分类的评测数据集中:1)大多数是一个句子一个目标;2)在少数有多个目标的句子中,多个目标情感倾向分布很不均衡,多个目标情感一致的情形占较大优势。数据集本身的缺陷限制了模型针对多个目标进行情感分类的提升空间。针对以上问题,本文构建了一个针对多目标情感分类的中文数据集,人工标注了6339个评价目标,共2071条数据。该数据集:1)评价目标个数分布平衡;2)情感正负极性分布平衡;3)多目标情感倾向分布平衡。随后,本文利用多个目标情感分类的主流模型在该数据集上进行了实验与比较分析。结果表明现有主流模型尚不能对存在多个目标且目标情感倾向性不一致实例中的目标进行很好的分类,尤其是目标的情感倾向为中性时。多目标情感分类任务具有一定的难度与挑战性。
基于Self-Attention的句法感知汉语框架语义角色标注(Syntax-Aware Chinese Frame Semantic Role Labeling Based on Self-Attention)
Authors: Xiaohui Wang, Ru Li, Zhiqiang Wang, Qinghua Chai, Xiaoqi Han
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10
Tasks: Semantic Role Labeling

框架语义角色标注(Frame Semantic Role Labeling, FSRL)是基于FrameNet标注体系的语义分析任务。语义角色标注通常对句法有很强的依赖性,目前的语义角色标注模型大多基于双向长短时记忆网络Bi-LSTM,虽然可以获取句子中的长距离依赖信息,但无法很好获取句子中的句法信息。因此,引入self-attention机制来捕获句子中每个词的句法信息。实验结果表明,该模型在CFN(Chinese FrameNet,汉语框架网)数据集上的F1达到83.77%,提升了近11%。
基于词语聚类的汉语口语教材自动推送素材研究(Study on Automatic Push Material of Oral Chinese Textbook Based on Word Clustering)
Authors: Bingbing Yang, Huizhou Zhao, Zhimin Wang
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10

新冠肺炎的蔓延使得线上移动教学成为教育发展的必然趋势,本文以适合汉语教材自动推送的口语素材为研究对象,基于10341条生活类口语语料,对词汇的整体特点进行计量分析,在此基础上使用词向量模型及Kmeans算法对全部词语进行聚类,参考词语聚类结果及对口语语料话题和场景的考察,构建了一个包含15个一级话题、102个二级话题及81个交际场景的汉语口语话题-场景素材库。同时对各级话题常用词进行了总结。本文可为教材自动定制的素材库提供资源支持。
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)
Authors: Hengrui Guo, Zhongqing Wang, Peifeng Li, Qiaoming Zhu
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10

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。
基于多粒度语义交互理解网络的幽默等级识别(A Multi-Granularity Semantic Interaction Understanding Network for Humor Level Recognition)
Authors: Jinhui Zhang, Shaowu Zhang, Xiaochao Fan, Liang Yang, Hongfei Lin
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10

幽默在人们日常交流中发挥着重要作用。随着人工智能的快速发展,幽默等级识别成为自然语言处理领域的热点研究问题之一。已有的幽默等级识别研究往往将幽默文本看作一个整体,忽视了幽默文本内部的语义关系。本文将幽默等级识别视为自然语言推理任务,将幽默文本划分为“铺垫”和“笑点”两个部分,分别对其语义和语义关系进行建模,提出了一种多粒度语义交互理解网络,从单词和子句两个粒度捕获幽默文本中语义的关联和交互。本文在Reddit公开幽默数据集上进行了实验,相比之前最优结果,模型在语料上的准确率提升了1.3%。实验表明,引入幽默内部的语义关系信息可以提高模型幽默识别的性能,而本文提出的模型也可以很好地建模这种语义关系。
文本情感分析中的重叠现象研究(A Study on Repetition in Text-based Sentiment Analysis)
Authors: Tuya Naren, Xiaoyin Xu
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10
Tasks: Sentiment Analysis

汉语中的重叠现象丰富,文本情感分析任务中应该密切关注语篇空间内的重叠现象及其交互状态。本文就重叠在文本中的样态、特点及情感标记功能进行了理论探讨;重点就构词性重叠、结构性重叠的表现形式及情感语义进行了分析;据此研究的基础上,本文就重叠现象在文本情感分析上的实际应用从几个方面进行了讨论。
基于BiLSTM-CRF的社会突发事件研判方法(Social Emergency Event Judgement based on BiLSTM-CRF)
Authors: Huijun Hu, Cong Wang, Jianhua Dai, Maofu Liu
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10

社会突发事件的分类和等级研判作为应急处置中的一环,其重要性不言而喻。然而,目前研究多数采用人工或规则的方法识别证据进行研判,由于社会突发事件的构成的复杂性和语言描述的灵活性,这对于研判证据识别有很大局限性。本文参考“事件抽取”思想,事件类型和研判证据作为事件中元素,以BiLSTM-CRF方法细粒度的识别,并将二者结合,分类结果作为等级研判的输入,识别出研判证据。最终将识别结果结合注意力机制进行等级研判,通过对研判证据的精准识别从而来增强等级研判的准确性。实验表明,相比人工或规则识别研判证据,本文提出的方法有着更好的鲁棒性,社会突发事件研判时也达到了较好的效果。 关键词:事件分类 ;研判证据识别 ;等级研判 ;BiLSTM-CRF
结合金融领域情感词典和注意力机制的细粒度情感分析(Attention-based Recurrent Network Combined with Financial Lexicon for Aspect-level Sentiment Classification)
Authors: Qinglin Zhu, Bin Liang, Liuyu Han, Yi Chen, Ruifeng Xu, Ruibin Mao
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10
Tasks: Sentiment Analysis, Sentiment Classification

针对在金融领域实体级情感分析任务中,往往缺乏足够的标注语料,以及通用的情感分析模型难以有效处理金融文本等问题。本文构建一个百万级别的金融领域实体情感分析语料库,并标注五千余个金融领域情感词作为金融领域情感词典。同时,基于该金融领域数据集,提出一种结合金融领域情感词典和注意力机制的金融文本细粒度情感分析模型。该模型使用两个LSTM网络分别提取词级别的语义信息和基于情感词典分类后的词类级别信息,能有效获取金融领域词语的特征信息。此外,为了让文本中金融领域情感词获得更多关注,提出一种基于金融领域情感词典的注意力机制来为不同实体获取重要的情感信息。最终在构建的金融领域实体级语料库上进行实验,取得了比对比模型更好的效果。
基于层次注意力机制和门机制的属性级别情感分析(Aspect-level Sentiment Analysis Based on Hierarchical Attention and Gate Networks)
Authors: Chao Feng, Haihui Li, Hongya Zhao, Yun Xue, Jingyao Tang
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10
Tasks: Sentiment Analysis

近年来,作为细粒度的属性级别情感分析在商业界和学术界受到越来越多的关注,其目的在于识别一个句子中多个属性词所对应的情感极性。目前,在解决属性级别情感分析问题的绝大多数工作都集中在注意力机制的设计上,以此突出上下文和属性词中不同词对于属性级别情感分析的贡献,同时使上下文和属性词之间相互关联。本文提出使用层次注意力机制和门机制处理属性级别情感分析任务,在得到属性词的隐藏状态之后,通过注意力机制得到属性词新的表示,然后利用属性词新的表示和注意力机制进一步得到上下文新的表示,层次注意力机制的设计使得上下文和属性词的表达更加准确;同时通过门机制选择对属性词而言上下文中有用的信息,以此丰富上下文的表达,在SemEval 2014 Task4和Twitter数据集上的实验结果表明本文提出模型的有效性。
基于循环交互注意力网络的问答立场分析(A Recurrent Interactive Attention Network for Answer Stance Analysis)
Authors: Wangda Luo, YuHan Liu, Bin Liang, Ruifeng Xu
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10

针对问答立场任务中,现有方法难以提取问答文本间的依赖关系问题,本文提出一种基于循环交互注意力(Recurrent Interactive Attention, RIA)网络的问答立场分析方法。该方法通过模仿人类阅读理解时的思维方式,基于交互注意力机制和循环迭代方法,有效地从问题和答案的相互联系中挖掘问答文本的立场信息。此外,该方法将问题进行陈述化表示,有效地解决疑问句表述下问题文本无法明确表达自身立场的问题。实验结果表明,本文方法取得了比现有模型方法更好的效果,同时证明该方法能有效拟合问答立场分析任务中的问答对依赖关系。
新型冠状病毒肺炎相关的推特主题与情感研究(Exploring COVID-19-related Twitter Topic Dynamics across Countries)
Authors: Shuailong Liang, Derek F. Wong, Yue Zhang
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10

我们基于从2020年1月22日至2020年4月30日在推特社交平台上抓取的不同国家和地区发布的50万条推文,研究了有关 2019新型冠状病毒肺炎相关的主题和人们的观点,发现了不同国家之间推特用户的普遍关切和看法之间存在着异同,并且对不同议题的情感态度也有所不同。我们发现大部分推文中包含了强烈的情感,其中表达爱与支持的推文比较普遍。总体来看,人们的情感随着时间的推移逐渐正向增强。
融入多尺度特征注意力的胶囊神经网络及其在文本分类中的应用(Incorporating Multi-scale Feature Attention into Capsule Network and its Application in Text Classification)
Authors: Chaofan Wang, Shenggen Ju, Jieping Sun, Run Chen
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10
Tasks: text-classification, Text Classification

近些年来,胶囊神经网络(Capsnets)由于拥有强大的文本特征学习能力已被应用到了文本分类任务当中。目前的研究工作大部分都将提取到的文本多元语法特征视为同等重要,而忽略了单词所对应各个多元语法特征的重要程度应该是由具体上下文决定的这一问题,这将直接影响到模型对整个文本的语义理解。针对上述问题,本文提出了多尺度特征部分连接胶囊网络(MulPart-Capsnets)。该方法将多尺度特征注意力融入到Capsnets中,多尺度特征注意力能够自动选择不同尺度的多元语法特征,通过对其进行加权求和,就能为每个单词精确捕捉到丰富的多元语法特征。同时,为了减少子胶囊与父胶囊之间的冗余信息传递,本文同时也对路由算法进行了改进。 本文提出的算法在文本分类任务上针对七个著名的数据集进行了有效性验证,和现有的研究工作相比,性能显著提高,说明了本文的算法能够捕获文本中更丰富的多元语法特征,具有更加强大的文本特征学习能力。
结合深度学习和语言难度特征的句子可读性计算方法(The method of calculating sentence readability combined with deep learning and language difficulty characteristics)
Authors: Yuling Tang, Dong Yu
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10
Tasks: Sentence

本文提出了可读性语料库构建的改进方法,基于该方法,构建了规模更大的汉语句子可读性语料库。该语料库在句子绝对难度评估任务上的准确率达到0.7869,相对前人工作提升了0.15以上,证明了改进方法的有效性。将深度学习方法应用于汉语可读性评估,探究了不同深度学习方法自动捕获难度特征的能力,并进仛步探究了向深度学习特征中融入不同层面的语难度特征对模型整体性能的影响。实验结果显示,不同深度学习模型的难度特征捕获能力不尽相同,语言难度特征可以不同程度地提高深度学习模型的难度表征能力。
基于预训练语言模型的案件要素识别方法(A Method for Case Factor Recognition Based on Pre-trained Language Models)
Authors: Haishun Liu, Lei Wang, Yanguang Chen, Shuchen Zhang, Yuanyuan Sun, Hongfei Lin
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10

案件要素识别指将案件描述中重要事实描述自动抽取出来,并根据领域专家设计的要素体系进行分类,是智慧司法领域的重要研究内容。基于传统神经网络的文本编码难以提取深层次特征,基于阈值的多标签分类难以捕获标签间依赖关系,因此本文提出了基于预训练语言模型的多标签文本分类模型。该模型采用以Layer-attentive策略进行特征融合的语言模型作为编码器,使用基于LSTM的序列生成模型作为解码器。在“CAIL2019”数据集上进行实验,该方法比基于循环神经网络的算法在F1值上最高可提升7.6%,在相同超参数设置下比基础语言模型(BERT)提升约3.2%。
基于拼音约束联合学习的汉语语音识别(Chinese Speech Recognition Based on Pinyin Constraint Joint Learning)
Authors: Renfeng Liang, Zhengtao Yu, Shengxiang Gao, Yuxin Huang, Junjun Guo, Shuli Xu
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10
Tasks: speech-recognition, Speech Recognition

当前的语音识别模型在英语、法语等表音文字中已经取得很好的效果。然而,汉语是 一种典型的表意文字,汉字与语音没有直接的对应关系,但拼音作为汉字读音的标注 符号,与汉字存在相互转换的内在联系。因此,在汉语语音识别中利用拼音作为解码 约束,引入一种更接近语音的归纳偏置。基于多任务学习框架,提出一种基于拼音约 束联合学习的汉语语音识别方法,以端到端的汉字语音识别为主任务,以拼音语音识 别为辅助任务,通过共享编码器,同时利用汉字与拼音识别结果作为监督信号,增强 编码器对汉语语音的表达能力。实验结果表明,相比基线模型,提出方法取得更优的 识别效果,词错误率WER降低了2.24个百分点
基于数据增强和多任务特征学习的中文语法错误检测方法(Chinese Grammar Error Detection based on Data Enhancement and Multi-task Feature Learning)
Authors: Haihua Xie, Zhiyou Chen, Jing Cheng, Xiaoqing Lyu, Zhi Tang
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10

由于中文语法的复杂性,中文语法错误检测(CGED)的难度较大,而训练语料和相关研究的缺乏,使得CGED的效果还远达不到能够实用的程度。本文提出一种CGED模型,采用数据增强、预训练语言模型和基于语言学特征多任务学习的方式,弥补训练语料稀缺的不足。数据增强能够有效地扩充训练集,预训练语言模型蕴含丰富的语义信息有助于语法分析,基于语言学特征多任务学习对语言模型进行微调则可以使语言模型学习到跟语法错误检测相关的语言学特征。本文提出的方法在NLPTEA的CGED数据集进行测试,取得了优于其他模型的结果。
基于有向异构图的发票明细税收分类方法(Tax Classification of Invoice Details Based on Directed Heterogeneous Graph)
Authors: Peiyao Zhao, Qinghua Zheng, Bo Dong, Jianfei Ruan, Minnan Luo
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10

税收是国家赖以生存的物质基础。为加快税收现代化,方便纳税人便捷、规范开具增值税发票,国税总局规定纳税人在税控系统开票前选择发票明细对应的税收分类才可正常开具发票。提高税收分类的准确度,是构建税收风险指标和分析纳税人行为特征的重要基础。基于此,本文提出了一种基于有向异构图的短文本分类模型(Heterogeneous Directed Graph Attenton Network,HDGAT),利用发票明细间的有向信息建模,引入外部知识,显著地提高了发票明细的税收分类准确度。
半监督跨领域语义依存分析技术研究(Semi-supervised Domain Adaptation for Semantic Dependency Parsing)
Authors: Dazhan Mao, Huayong Li, Yanqiu Shao
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10
Tasks: Dependency Parsing, Domain Adaptation, Semantic Dependency Parsing, Semi-supervised Domain Adaptation

近年来,尽管深度学习给语义依存分析带来了长足的进步,但由于语义依存分析数据标注代价非常高昂,并且在单领域上性能较好的依存分析器迁移到其他领域时,其性能会大幅度下降。因此为了使其走向实用,就必须解决领域适应问题。本文提出一个新的基于对抗学习的领域适应依存分析模型,我们提出了基于对抗学习的共享双编码器结构,并引入领域私有辅助任务和正交约束,同时也探究了多种预训练模型在跨领域依存分析任务上的效果和性能。
汉英篇章衔接对齐语料构建研究(Research on the Construction of Chinese-English Discourse Cohesion Alignment Corpus)
Authors: Yancui Li, Jike Feng, Chunxiao Lai, Hongyu Feng
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10

篇章衔接性分析是理解篇章的基础,汉语和英语在指代、连接和省略等主要衔接方式上存在差异。本文旨在创建汉英篇章衔接对齐语料库,给出包括子句、连接词、指代和省略的汉英篇章衔接对齐标注策略,建立包含相应信息的对齐信息的语料库资源,最后对标注语料进行评估并讨论了标注中的难点问题及解决方法。对语料库标注质量评估及简单实验结果表明,本文研究语料标注策略方法切实可行,所标注的资源一致性满足实际需要。
Cross-Lingual Dependency Parsing via Self-Training
Authors: Meishan Zhang, Yue Zhang
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10
Tasks: Cross-Lingual POS Tagging, Cross-Lingual Transfer, Dependency Parsing, POS, POS Tagging

Recent advances of multilingual word representations weaken the input divergences across languages, making cross-lingual transfer similar to the monolingual cross-domain and semi-supervised settings. Thus self-training, which is effective for these settings, could be possibly beneficial to cross-lingual as well. This paper presents the first comprehensive study for self-training in cross-lingual dependency parsing. Three instance selection strategies are investigated, where two of which are based on the baseline dependency parsing model, and the third one adopts an auxiliary cross-lingual POS tagging model as evidence. We conduct experiments on the universal dependencies for eleven languages. Results show that self-training can boost the dependency parsing performances on the target languages. In addition, the POS tagger assistant instance selection can achieve further improvements consistently. Detailed analysis is conducted to examine the potentiality of self-training in-depth.
A Joint Model for Graph-based Chinese Dependency Parsing
Authors: Xingchen Li, Mingtong Liu, Yujie Zhang, Jinan Xu, Yufeng Chen
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10
Tasks: Chinese Dependency Parsing, Chinese Word Segmentation, Dependency Parsing, POS, POS Tagging, Segmentation, TAG

In Chinese dependency parsing, the joint model of word segmentation, POS tagging and dependency parsing has become the mainstream framework because it can eliminate error propagation and share knowledge, where the transition-based model with feature templates maintains the best performance. Recently, the graph-based joint model (Yan et al., 2019) on word segmentation and dependency parsing has achieved better performance, demonstrating the advantages of the graph-based models. However, this work can not provide POS information for downstream tasks, and the POS tagging task was proved to be helpful to the dependency parsing according to the research of the transition-based model. Therefore, we propose a graph-based joint model for Chinese word segmentation, POS tagging and dependency parsing. We designed a charater-level POS tagging task, and then train it jointly with the model of Yan et al. (2019). We adopt two methods of joint POS tagging task, one is by sharing parameters, the other is by using tag attention mechanism, which enables the three tasks to better share intermediate information and improve each other’s performance. The experimental results on the Penn Chinese treebank (CTB5) show that our proposed joint model improved by 0.38% on dependency parsing than the model of Yan et al. (2019). Compared with the best transition-based joint model, our model improved by 0.18%, 0.35% and 5.99% respectively in terms of word segmentation, POS tagging and dependency parsing.
Semantic-aware Chinese Zero Pronoun Resolution with Pre-trained Semantic Dependency Parser
Authors: Lanqiu Zhang, Zizhuo Shen, Yanqiu Shao
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10
Tasks: Chinese Zero Pronoun Resolution, Deep Learning

Deep learning-based Chinese zero pronoun resolution model has achieved better performance than traditional machine learning-based model. However, the existing work related to Chinese zero pronoun resolution has not yet well integrated linguistic information into the deep learningbased Chinese zero pronoun resolution model.This paper adopts the idea based on the pre-trained model, and integrates the semantic representations in the pre-trained Chinese semantic dependency graph parser into the Chinese zero pronoun resolution model. The experimental results on OntoNotes-5.0 dataset show that our proposed Chinese zero pronoun resolution model with pretrained Chinese semantic dependency parser improves the F-score by 0.4% compared with our baseline model, and obtains better results than other deep learning-based Chinese zero pronoun resolution models. In addition, we integrate the BERT representations into our model so that the performance of our model was improved by 0.7% compared with our baseline model.
Improving Sentence Classification by Multilingual Data Augmentation and Consensus Learning
Authors: Yanfei Wang, Yangdong Chen, Yuejie Zhang
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10
Tasks: Classification, Data Augmentation, Machine Translation, Sentence, Sentence Classification, Translation

Neural network based models have achieved impressive results on the sentence classification task. However, most of previous work focuses on designing more sophisticated network or effective learning paradigms on monolingual data, which often suffers from insufficient discriminative knowledge for classification. In this paper, we investigate to improve sentence classification by multilingual data augmentation and consensus learning. Comparing to previous methods, our model can make use of multilingual data generated by machine translation and mine their language-share and language-specific knowledge for better representation and classification. We evaluate our model using English (i.e., source language) and Chinese (i.e., target language) data on several sentence classification tasks. Very positive classification performance can be achieved by our proposed model.
Attention-Based Graph Neural Network with Global Context Awareness for Document Understanding
Authors: Yuan Hua, Zheng Huang, Jie Guo, Weidong Qiu
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10
Tasks: document understanding, graph construction, Graph Neural Network

Information extraction from documents such as receipts or invoices is a fundamental and crucial step for office automation. Many approaches focus on extracting entities and relationships from plain texts, however, when it comes to document images, such demand becomes quite challenging since visual and layout information are also of great significance to help tackle this problem. In this work, we propose the attention-based graph neural network to combine textual and visual information from document images.Moreover, the global node is introduced in our graph construction algorithm which is used as a virtual hub to collect the information from all the nodes and edges to help improve the performance. Extensive experiments on real-world datasets show that our method outperforms baseline methods by significant margins.
Combining Impression Feature Representation for Multi-turn Conversational Question Answering
Authors: Shaoling Jing, Shibo Hong, Dongyan Zhao, Haihua Xie, Zhi Tang
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10
Tasks: Conversational Question Answering, feature selection, Question Answering

Multi-turn conversational Question Answering (ConvQA) is a practical task that requires the understanding of conversation history, such as previous QA pairs, the passage context, and current question. It can be applied to a variety of scenarios with human-machine dialogue. The major challenge of this task is to require the model to consider the relevant conversation history while understanding the passage. Existing methods usually simply prepend the history to the current question, or use the complicated mechanism to model the history. This article proposes an impression feature, which use the word-level inter attention mechanism to learn multi-oriented information from conversation history to the input sequence, including attention from history tokens to each token of the input sequence, and history turn inter attention from different history turns to each token of the input sequence, and self-attention within input sequence, where the input sequence contains a current question and a passage. Then a feature selection method is designed to enhance the useful history turns of conversation and weaken the unnecessary information. Finally, we demonstrate the effectiveness of the proposed method on the QuAC dataset, analyze the impact of different feature selection methods, and verify the validity and reliability of the proposed features through visualization and human evaluation.
Chinese Long and Short Form Choice Exploiting Neural Network Language Modeling Approaches
Authors: Lin Li, Kees Van Deemter, Denis Paperno
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10
Tasks: Form, Language Modeling, Language Modelling, Natural Language Understanding

This paper presents our work in long and short form choice, a significant question of lexical choice, which plays an important role in many Natural Language Understanding tasks. Long and short form sharing at least one identical word meaning but with different number of syllables is a highly frequent linguistic phenomenon in Chinese like 老虎-虎(laohu-hu, tiger)
Refining Data for Text Generation
Authors: Wenyu Guan, Qianying Liu, Tianyi Li, Sujian Li
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10
Tasks: Data-to-Text Generation, Decoder, Learning-To-Rank, Text Generation

Recent work on data-to-text generation has made progress under the neural encoder-decoder architectures. However, the data input size is often enormous, while not all data records are important for text generation and inappropriate input may bring noise into the final output. To solve this problem, we propose a two-step approach which first selects and orders the important data records and then generates text from the noise-reduced data. Here we propose a learning to rank model to rank the importance of each record which is supervised by a relation extractor. With the noise-reduced data as input, we implement a text generator which sequentially models the input data records and emits a summary. Experiments on the ROTOWIRE dataset verifies the effectiveness of our proposed method in both performance and efficiency.
Plan-CVAE: A Planning-based Conditional Variational Autoencoder for Story Generation
Authors: Lin Wang, Juntao Li, Rui Yan, Dongyan Zhao
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10
Tasks: Diversity, Story Generation

Story generation is a challenging task of automatically creating natural languages to describe a sequence of events, which requires outputting text with not only a consistent topic but also novel wordings. Although many approaches have been proposed and obvious progress has been made on this task, there is still a large room for improvement, especially for improving thematic consistency and wording diversity. To mitigate the gap between generated stories and those written by human writers, in this paper, we propose a planning-based conditional variational autoencoder, namely Plan-CVAE, which first plans a keyword sequence and then generates a story based on the keyword sequence. In our method, the keywords planning strategy is used to improve thematic consistency while the CVAE module allows enhancing wording diversity. Experimental results on a benchmark dataset confirm that our proposed method can generate stories with both thematic consistency and wording novelty, and outperforms state-of-the-art methods on both automatic metrics and human evaluations.
Named Entity Recognition with Context-Aware Dictionary Knowledge
Authors: Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Named entity recognition (NER) is an important task in the natural language processing field. Existing NER methods heavily rely on labeled data for model training, and their performance on rare entities is usually unsatisfactory. Entity dictionaries can cover many entities including both popular ones and rare ones, and are useful for NER. However, many entity names are context-dependent and it is not optimal to directly apply dictionaries without considering the context. In this paper, we propose a neural NER approach which can exploit dictionary knowledge with contextual information. We propose to learn context-aware dictionary knowledge by modeling the interactions between the entities in dictionaries and their contexts via context-dictionary attention. In addition, we propose an auxiliary term classification task to predict the types of the matched entity names, and jointly train it with the NER model to fuse both contexts and dictionary knowledge into NER. Extensive experiments on the CoNLL-2003 benchmark dataset validate the effectiveness of our approach in exploiting entity dictionaries to improve the performance of various NER models.
Chinese Named Entity Recognition via Adaptive Multi-pass Memory Network with Hierarchical Tagging Mechanism
Authors: Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao
Date: 2020-10-01 00:00:00
Proceeding: CCL 2020 10
Tasks: Chinese Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, TAG

Named entity recognition (NER) aims to identify text spans that mention named entities and classify them into pre-defined categories. For Chinese NER task, most of the existing methods are character-based sequence labeling models and achieve great success. However, these methods usually ignore lexical knowledge, which leads to false prediction of entity boundaries. Moreover, these methods have difficulties in capturing tag dependencies. In this paper, we propose an Adaptive Multi-pass Memory Network with Hierarchical Tagging Mechanism (AMMNHT) to address all above problems. Specifically, to reduce the errors of predicting entity boundaries, we propose an adaptive multi-pass memory network to exploit lexical knowledge. In addition, we propose a hierarchical tagging layer to learn tag dependencies. Experimental results on three widely used Chinese NER datasets demonstrate that our proposed model significantly outperforms other state-of-the-art methods.