Das L3S ist ein Forschungszentrum für grundlagen- und anwendungsorientierte Forschung im Bereich Digitale Transformation sowie Data- und Web-Science. L3S-Forscher entwickeln zukunftsweisende Methoden und Informationstechnologien für intelligente, zuverlässige und verantwortungsvolle Systeme, welche zur Gestaltung der digitalen Transformation und zur nachhaltigen Innovation beitragen. Der Wissentransfer in Wirtschaft, Politik und Gesellschaft bildet einen zentralen Baustein der Aktivitäten des L3S.

Das L3S erforscht die Auswirkungen des digitalen Wandels, um aus den Erkenntnissen Handlungsoptionen, -empfehlungen sowie Innovationsstrategien für die Wirtschaft, die Politik und Gesellschaft herzuleiten. Durch Forschung, Entwicklung und Beratung trägt das L3S gemeinsam mit seinen Partnern zur digitalen Transformation insbesondere in den Bereichen Mobilität, Gesundheit, Produktion und Bildung bei.

Das L3S ist eine gemeinsame Einrichtung der Leibniz Universität Hannover und der Technischen Universität Braunschweig mit rund 150 Wissenschaftlern aus unterschiedlichen Disziplinen an Universitäten und einem Leibniz-Institut (der TIB). Das aktuelle jährliche Drittmittelvolumen des L3S beträgt etwa 6 Mio. Euro, Niedersachsen fördert das L3S zusätzlich mit jährlich etwa 2 Mio. Euro. Das L3S wurde 2011 und 2014 vom Wissenschaftsrat als exzellente Einrichtung evaluiert, führend im Bereich Web Science.

• Beyond Understanding: The under-Explored Role of Affect in Student-AI Interactions. Klumbytė, Goda; Stöter, Lea; Lackner, Konstantin; Horn, Viktoria; Uhlmann, Markus; Knoth, Nils; Bläsing, Lisa Marie; Draude, Claude R. M. Davison, H. Tellioglu (eds.) (2027). 135–152.

  This paper presents findings of a small-scale qualitative interview study of students' use of generative AI tools in their study practices at a German University, and the affective dimensions that emerged in AI understanding and use. We report how affective dimensions manifested through various sites of “(gut) feelings” in AI evaluation and verification as well as in AI use practices, constituting three types of affect-as-resource, namely epistemic resource, access and emotional scaffolding, and affect as interaction resource. We argue that, beyond understanding how individual affective states of the user might affect generative AI understanding and use practices, it is important to critically address affect as a relational domain of agency.
• Beyond Understanding: The under-Explored Role of Affect in Student-AI Interactions. Klumbytė, Goda; Stöter, Lea; Lackner, Konstantin; Horn, Viktoria; Uhlmann, Markus; Knoth, Nils; Bläsing, Lisa Marie; Draude, Claude R. M. Davison, H. Tellioglu (eds.) (2027). 135–152.

  This paper presents findings of a small-scale qualitative interview study of students' use of generative AI tools in their study practices at a German University, and the affective dimensions that emerged in AI understanding and use. We report how affective dimensions manifested through various sites of “(gut) feelings” in AI evaluation and verification as well as in AI use practices, constituting three types of affect-as-resource, namely epistemic resource, access and emotional scaffolding, and affect as interaction resource. We argue that, beyond understanding how individual affective states of the user might affect generative AI understanding and use practices, it is important to critically address affect as a relational domain of agency.
• Beyond Understanding: The under-Explored Role of Affect in Student-AI Interactions. Klumbytė, Goda; Stöter, Lea; Lackner, Konstantin; Horn, Viktoria; Uhlmann, Markus; Knoth, Nils; Bläsing, Lisa Marie; Draude, Claude R. M. Davison, H. Tellioglu (eds.) (2027). 135–152.

  This paper presents findings of a small-scale qualitative interview study of students' use of generative AI tools in their study practices at a German University, and the affective dimensions that emerged in AI understanding and use. We report how affective dimensions manifested through various sites of “(gut) feelings” in AI evaluation and verification as well as in AI use practices, constituting three types of affect-as-resource, namely epistemic resource, access and emotional scaffolding, and affect as interaction resource. We argue that, beyond understanding how individual affective states of the user might affect generative AI understanding and use practices, it is important to critically address affect as a relational domain of agency.
• Nice to Need You! Psychological need fulfillment, well-being, and technology acceptance in human--robot interaction across contexts. Klier, Chantal; Lugrin, Birgit (2026). 9 100368.
• Current morphodynamics and subsurface structure of thrust moraines and rock glaciers connected to three Little Ice Age glacier forefields in the Swiss Alps. Kunz, J.; Buchelt, S.; Wiegand, T.; Ullmann, T.; Kneisel, C. (2026). 20(8) 4255–4275.
• SPAGBias: Uncovering and Tracing Structured Spatial Gender Bias in Large Language Models. Su, Binxian; Lou, Haoye; Zhu, Shucheng; Wang, Weikang; Liu, Ying; Yu, Dong; Liu, Pengyuan M. Liakata, V. P. Moreira, J. Zhang, D. Jurgens (eds.) (2026). 1222–1251.
• Evaluating Moral Beliefs across LLMs through a Pluralistic Framework. Liu, Xuelin; Zhu, Yanfei; Zhu, Shucheng; Liu, Pengyuan; Liu, Ying; Yu, Dong in Findings of ACL, Y. Al-Onaizan, M. Bansal, Y.-N. Chen (eds.) (2024). (Vol. EMNLP 2024) 4740–4760.
• MSDD: A Multimodal Language Dateset for Stance Detection. Hu, Mengyang; Liu, Pengyuan; Wang, Weikang; Zhang, Hu; Lin, Chengxiao in Lecture Notes in Computer Science, Q. Su, G. Xu, X. Yang (eds.) (2022). (Vol. 13495) 112–124.
• 基于组块分析的汉语块依存语法(Chinese Chunk-Based Dependency Grammar). Qian, Qingqing; Wang, Chengwen; Rao, Gaoqi; Xun, Endong M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 117–127.
• Categorizing Offensive Language in Social Networks: A Chinese Corpus, Systems and an Explainable Tool. Tang, Xiangru; Shen, Xianjun M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 1045–1056.
• Semantic-aware Chinese Zero Pronoun Resolution with Pre-trained Semantic Dependency Parser. Zhang, Lanqiu; Shen, Zizhuo; Shao, Yanqiu M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 831–841.
• 基于抽象语义表示的汉语疑问句的标注与分析(Chinese Interrogative Sentences Annotation and Analysis Based on the Abstract Meaning Representation). Yan, Peiyi; Li, Bin; Huang, Tong; Huo, Kairui; Chen, Jin; Qu, Weiguang M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 77–87.
• 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). Xie, Haihua; Chen, Zhiyou; Cheng, Jing; Lyu, Xiaoqing; Tang, Zhi M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 761–770.
• Combining Impression Feature Representation for Multi-turn Conversational Question Answering. Jing, Shaoling; Hong, Shibo; Zhao, Dongyan; Xie, Haihua; Tang, Zhi M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 863–873.
• 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). Zhao, Peiyao; Zheng, Qinghua; Dong, Bo; Ruan, Jianfei; Luo, Minnan M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 771–782.
• 基于神经网络的连动句识别(Recognition of serial-verb sentences based on Neural Network). Sun, Chao; Qu, Weiguang; Wei, Tingxin; Gu, Yanhui; Li, Bin; Zhou, Junsheng M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 172–182.
• 基于多语言联合训练的汉-英-缅神经机器翻译方法(Chinese-English-Burmese Neural Machine Translation Method Based on Multilingual Joint Training). Man, Zhibo; Mao, Cunli; Yu, Zhengtao; Li, Xunyu; Gao, Shengxiang; Zhu, Junguo M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 446–456.
• 汉语学习者依存句法树库构建(Construction of a Treebank of Learner Chinese). Shi, Jialu; Luo, Xinyu; Yang, Liner; Xiao, Dan; Hu, Zhengsheng; Wang, Yijun; Yuan, Jiaxin; Jingsi, Yu; Yang, Erhong M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 581–592.
• 文本情感分析中的重叠现象研究(A Study on Repetition in Text-based Sentiment Analysis). Naren, Tuya; Xu, Xiaoyin M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 656–666.
• 融合全局和局部信息的汉语宏观篇章结构识别(Combining Global and Local Information to Recognize Chinese Macro Discourse Structure). Fan, Yaxin; Jiang, Feng; Chu, Xiaomin; Li, Peifeng; Zhu, Qiaoming M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 183–194.
• 基于Graph Transformer的知识库问题生成(Question Generation from Knowledge Base with Graph Transformer). Hu, Yue; Zhou, Guangyou M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 324–335.
• 一种基于相似度的藏文词同现网络构建及特征分析(A Research on Construction and Feature Analysis of Similarity-based Tibetan Word Co-occurrence Networks). Jiayang, Dongzhou; Cai, Zhijie; Cairang, Zhuoma; San, Maocuo M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 509–517.
• 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). Wang, Xiaohui; Li, Ru; Wang, Zhiqiang; Chai, Qinghua; Han, Xiaoqi M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 616–623.
• 基于多任务学习的生成式阅读理解(Generative Reading Comprehension via Multi-task Learning). Qian, Jin; Huang, Rongtao; Zou, Bowei; Hong, Yu M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 301–312.
• 基于语料库的武侠与仙侠网络小说文体、词汇及主题对比分析(A Corpus-based Contrastive Analysis of Style, Vocabulary and Theme of Wuxia and Xianxia Internet Novels). Zhang, Sanle; Liu, Pengyuan; Zhang, Hu M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 10–19.
• Better Queries for Aspect-Category Sentiment Classification. Li, Yuncong; Yin, Cunxiang; hua Zhong, Sheng; Zhong, Huiqiang; Luo, Jinchang; Xu, Siqi; Wu, Xiaohui M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 1079–1088.
• 汉语块依存语法与树库构建(Chinese Chunk-Based Dependency Grammar and Treebank construction). Qian, Qingqing; Wang, Chengwen M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 572–580.
• 基于BERT与柱搜索的中文释义生成(Chinese Definition Modeling Based on BERT and Beam Seach). Fan, Qinan; Kong, Cunliang; Yang, Liner; Yang, Erhong M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 336–348.
• 面向汉语作为第二语言学习的个性化语法纠错(Personalizing Grammatical Error Correction for Chinese as a Second Language). Zhang, Shengsheng; Pang, Guina; Yang, Liner; Wang, Chencheng; Du, Yongping; Yang, Erhong; Huang, Yaping M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 97–106.
• 面向司法领域的高质量开源藏汉平行语料库构建(A High-quality Open Source Tibetan-Chinese Parallel Corpus Construction of Judicial Domain). Sha, Jiu; Zhou, Luqin; Feng, Chong; Li, Hongzheng; Zhang, Tianfu; Hui, Hui M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 499–508.
• Constructing Uyghur Name Entity Recognition System using Neural Machine Translation Tag Projection. Anwar, Azmat; Xiao, Li; Yang, Yating; Rui, Dong; Turghun, Osman M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 1006–1016.
• 新支话题的句法成分和语义角色研究(A Study of Syntactic Constituent and Semantic Role of New Branch Topic). Lu, Dawei M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 128–140.
• 小样本关系分类研究综述(Few-Shot Relation Classification: A Survey). Hu, Han; Liu, Pengyuan M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 363–375.
• 面向微博文本的融合字词信息的轻量级命名实体识别(Lightweight Named Entity Recognition for Weibo Based on Word and Character). Chen, Chun; Li, Mingyang; Kong, Fang M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 402–413.
• 汉语否定焦点识别研究: 数据集与基线系统(Research on Chinese Negative Focus Identification: Dataset and Baseline). Sheng, Jiaxuan; Zou, Bowei; Shen, Longxiang; Ye, Jing; Hong, Yu M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 550–560.
• Clickbait Detection with Style-aware Title Modeling and Co-attention. Wu, Chuhan; Wu, Fangzhao; Qi, Tao; Huang, Yongfeng M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 1143–1154.
• Named Entity Recognition with Context-Aware Dictionary Knowledge. Wu, Chuhan; Wu, Fangzhao; Qi, Tao; Huang, Yongfeng M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 915–926.
• 基于子词级别词向量和指针网络的朝鲜语句子排序(Korean Sentence Ordering Based on Sub Word Level Word Vector and Pointer Network). Yan, Xiaodong; Xie, Xiaoqing M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 467–478.
• 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). Luo, Wangda; Liu, Yuhan; Liang, Bin; Xu, Ruifeng M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 698–706.
• 基于对话约束的回复生成研究(Research on Response Generation via Dialogue Constraints). Guan, Mengyu; Wang, Zhongqing; Li, Shoushan; Zhou, Guodong M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 225–235.
• Refining Data for Text Generation. Guan, Wenyu; Liu, Qianying; Li, Tianyi; Li, Sujian M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 881–891.
• CDCPP: 跨领域中文标点符号预测(CDCPP: Cross-Domain Chinese Punctuation Prediction). Liu, Pengyuan; Wang, Weikang; Qiu, Likun; Du, Bingjie M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 593–603.
• 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). Yang, Bingbing; Zhao, Huizhou; Wang, Zhimin M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 624–633.
• 面向中文AMR标注体系的兼语语料库构建及识别研究(Research on the Construction and Recognition of Concurrent corpus for Chinese AMR Annotation System). Hou, Wenhui; Qu, Weiguang; Wei, Tingxin; Li, Bin; Gu, Yanhui; Zhou, Junsheng M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 528–538.
• Frontmatter. Sun, Maosong; Li, Sujian; Zhang, Yue; Liu, Yang (2020).
• 眼动记录与主旨结构标注的关联性分析研究(Research on the correlation between eye movement feature and thematic structure label). Shan, Haocong; Zhou, Qiang M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 141–152.
• 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). Wang, Chaofan; Ju, Shenggen; Sun, Jieping; Chen, Run M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 719–730.
• Mongolian Questions Classification Based on Mulit-Head Attention. Wang, Guangyi; Bao, Feilong; Wang, Weihua M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 1026–1034.
• 引入源端信息的机器译文自动评价方法研究(Research on Incorporating the Source Information to Automatic Evaluation of Machine Translation). Luo, Qi; Li, Maoxi M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 414–423.
• 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). Guo, Hengrui; Wang, Zhongqing; Li, Peifeng; Zhu, Qiaoming M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 634–644.
• A Practice of Tourism Knowledge Graph Construction based on Heterogeneous Information. Xiao, Dinghe; Wang, Nannan; Yu, Jiangang; Zhang, Chunhong; Wu, Jiaqi M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 939–949.
• Multi-task Legal Judgement Prediction Combining a Subtask of Seriousness of Charge. Xu, Zhuopeng; Li, Xia; Li, Yinlin; Wang, Zihan; Fanxu, Yujie; Lai, Xiaoyan M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 1132–1142.
• 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). Li, Yancui; Feng, Jike; Lai, Chunxiao; Feng, Hongyu M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 795–806.
• 基于统一模型的藏文新闻摘要(Abstractive Summarization of Tibetan News Based on Hybrid Model). Yan, Xiaodong; Xie, Xiaoqing; Zou, Yu; Li, Wei M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 479–490.
• 基于多头注意力和BiLSTM改进DAM模型的中文问答匹配方法(Chinese question answering method based on multi-head attention and BiLSTM improved DAM model). Qin, Hanzhong; Yu, Chongchong; Jiang, Weijie; Zhao, Xia M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 313–323.
• Cross-Lingual Dependency Parsing via Self-Training. Zhang, Meishan; Zhang, Yue M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 807–819.
• Multi-Reward based Reinforcement Learning for Neural Machine Translation. Sun, Shuo; Hou, Hongxu; Wu, Nier; Guo, Ziyue; Zhang, Chaowei M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 984–993.
• Plan-CVAE: A Planning-based Conditional Variational Autoencoder for Story Generation. Wang, Lin; Li, Juntao; Yan, Rui; Zhao, Dongyan M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 892–902.
• 伟大的男人和倔强的女人: 基于语料库的形容词性别偏度历时研究(Great Males and Stubborn Females: A Diachronic Study of Corpus-Based Gendered Skewness in Chinese Adjectives). Zhu, Shucheng; Liu, Pengyuan M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 31–42.
• 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). Liang, Renfeng; Yu, Zhengtao; Gao, Shengxiang; Huang, Yuxin; Guo, Junjun; Xu, Shuli M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 754–760.
• Compress Polyphone Pronunciation Prediction Model with Shared Labels. Chen, Pengfei; Wang, Lina; Di, Hui; Ouchi, Kazushige; Wang, Lvhong M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 1122–1131.
• Entity Relative Position Representation based Multi-head Selection for Joint Entity and Relation Extraction. Zhao, Tianyang; Yan, Zhao; Cao, Yunbo; Li, Zhoujun M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 962–973.
• 基于计量的百年中国人名用字性别特征研究(A Quantified Research on Gender Characteristics of Chinese Names in A Century). Du, Bingjie; Liu, Pengyuan; Tian, Yongsheng M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 20–30.
• A Joint Model for Aspect-Category Sentiment Analysis with Shared Sentiment Prediction Layer. Li, Yuncong; Yang, Zhe; Yin, Cunxiang; Pan, Xu; Cui, Lunan; Huang, Qiang; Wei, Ting M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 1112–1121.
• 基于BERT的端到端中文篇章事件抽取(A BERT-based End-to-End Model for Chinese Document-level Event Extraction). Zhang, Hongkuan; Song, Hui; Wang, Shuyi; Xu, Bo M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 390–401.
• WAE_RN: Integrating Wasserstein Autoencoder and Relational Network for Text Sequence. Zhang, Xinxin; Liu, Xiaoming; Yang, Guan; Li, Fangfang M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 1172–1182.
• 半监督跨领域语义依存分析技术研究(Semi-supervised Domain Adaptation for Semantic Dependency Parsing). Mao, Dazhan; Li, Huayong; Shao, Yanqiu M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 783–794.
• Chinese and English Elementary Discourse Units Segmentation based on Bi-LSTM-CRF Model. Li, Yancui; Lai, Chunxiao; Feng, Jike; Feng, Hongyu M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 1068–1078.
• 发音属性优化建模及其在偏误检测的应用(Speech attributes optimization modeling and application in mispronunciation detection). Guo, Minghao; Xie, Yanlu M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 66–76.
• 联合依存分析的汉语语义组合模型(Chinese Semantic Composition Model with Dependency Parsing). Chen, Yuanmeng; Zhang, Yujie; Xu, Jinan; Chen, Yufeng M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 215–224.
• 基于规则的双重否定识别 - - 以"不v1不v2"为例(Double Negative Recognition Based on Rules - - Taking "不v1不v2" as an Example). Wang, Yu M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 1–9.
• 基于强负采样的词嵌入优化算法(Word Embedding Optimization Based on Hard Negative Sampling). Wang, Yuchen; Lin, Miaozhe; Zhan, Jiefan M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 207–214.
• A Study on the Classification of Chinese Interrogatives Based on Pragmatic Functions. Li, Jiangtao; Rao, Gaoqi in Lecture Notes in Computer Science, M. Liu, C. Kit, Q. Su (eds.) (2020). (Vol. 12278) 734–746.
• BIPRE: Bi-directional Inter-Personal Relationship Extraction Task. Tian, Yongsheng; Liu, Pengyuan; Du, Bingjie in Lecture Notes in Computer Science, M. Liu, C. Kit, Q. Su (eds.) (2020). (Vol. 12278) 640–651.
• A Dual Layer Regression Model for Cross-border E-commerce Industry Sale and Hot Product Prediction. Luo, Wangda; Su, Hang; Liu, Yuhan; Xu, Ruifeng in Lecture Notes in Computer Science, Y. Yang, L. Yu, L.-J. Zhang (eds.) (2020). (Vol. 12408) 50–61.
• 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). Liu, Pengyuan; Tian, Yongsheng; Du, Chengyu; Qiu, Likun M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 604–615.
• 基于跨语言双语预训练及Bi-LSTM的汉-越平行句对抽取方法(Chinese-Vietnamese Parallel Sentence Pair Extraction Method Based on Cross-lingual Bilingual Pre-training and Bi-LSTM). Liu, Chang; Gao, Shengxiang; Yu, Zhengtao; Huang, Yuxin; You, Congcong M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 457–466.
• 一种结合话语伪标签注意力的人机对话意图分类方法(A Human-machine Dialogue Intent Classification Method using Utterance Pseudo Label Attention). Ding, Jiande; Huang, Peijie; Xu, Jiabao; Peng, Youming M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 277–287.
• 融合目标端句法的AMR-to-Text生成(AMR-to-Text Generation with Target Syntax). Zhu, Jie; Li, Junhui M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 162–171.
• 多模块联合的阅读理解候选句抽取(Evidence sentence extraction for reading comprehension based on multi-module). Ji, Yu; Wang, Xiaoyue; Li, Ru; Guo, Shaoru; Guan, Yong M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 236–245.
• 多轮对话的篇章级抽象语义表示标注体系研究(Research on Discourse-level Abstract Meaning Representation Annotation framework in Multi-round Dialogue). Huang, Tong; Li, Bin; Yan, Peiyi; Ji, Tingting; Qu, Weiguang M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 54–65.
• A Novel Joint Framework for Multiple Chinese Events Extraction. Xu, Nuo; Xie, Haihua; Zhao, Dongyan M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 950–961.
• 基于图神经网络的汉语依存分析和语义组合计算联合模型(Joint Learning Chinese Dependency Parsing and Semantic Composition based on Graph Neural Network). Wang, Kai; Liu, Mingtong; Chen, Yuanmeng; Zhang, Yujie; Xu, Jinan; Chen, Yufeng M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 195–206.
• 融入对话上文整体信息的层次匹配回应选择(Learning Overall Dialogue Information for Dialogue Response Selection). Si, Bowen; Kong, Fang M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 266–276.
• A Mixed Learning Objective for Neural Machine Translation. Lu, Wenjie; Zhou, Leiying; Liu, Gongshen; Zhang, Quanhai M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 974–983.
• 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). Liang, Shuailong; Wong, Derek F.; Zhang, Yue M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 707–718.
• Improving Sentence Classification by Multilingual Data Augmentation and Consensus Learning. Wang, Yanfei; Chen, Yangdong; Zhang, Yuejie M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 842–852.
• Reusable Phrase Extraction Based on Syntactic Parsing. Duan, Xuemin; Zan, Hongying; Bai, Xiaojing; Zähner, Christoph M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 1166–1171.
• 《动词句法语义信息词典》知识内容说明书(An Introduction to the Syntactic-Semantic Knowledge-Base of Chinese Verbs). Yuan, Yulin; Cao, Hong M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 518–527.
• 中文问句的形式分类和资源建设(Formal classification and resource construction of Chinese questions). Li, Jiangtao; Rao, Gaoqi M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 107–116.
• 蒙古文拼写形式多样化现象研究(A Study of Spelling Variety of Mongolian). Bai, Shuangcheng; Hu, Sile M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 491–498.
• Attention-Based Graph Neural Network with Global Context Awareness for Document Understanding. Hua, Yuan; Huang, Zheng; Guo, Jie; Qiu, Weidong M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 853–862.
• 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). Zhang, Jinhui; Zhang, Shaowu; Fan, Xiaochao; Yang, Liang; Lin, Hongfei M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 645–655.
• 用计量风格学方法考察《水浒传》的作者争议问题 - - 以罗贯中《平妖传》为参照(Quantitive Stylistics Based Research on the Controversy of the Author of "Tales of the Marshes": Comparing with "Pingyaozhuan" of Luo Guanzhong). Song, Li; Liu, Ying M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 43–53.
• 基于层次化语义框架的知识库属性映射方法(Property Mapping in Knowledge Base Under the Hierarchical Semantic Framework). Li, Yu; Zhou, Guangyou M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 246–255.
• Chinese Long and Short Form Choice Exploiting Neural Network Language Modeling Approaches. Li, Lin; van Deemter, Kees; Paperno, Denis M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 874–880.
• Konwledge-Enabled Diagnosis Assistant Based on Obstetric EMRs and Knowledge Graph. Zhang, Kunli; Zhao, Xu; Zhuang, Lei; Xie, Qi; Zan, Hongying M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 1155–1165.
• A Joint Model for Graph-based Chinese Dependency Parsing. Li, Xingchen; Liu, Mingtong; Zhang, Yujie; Xu, Jinan; Chen, Yufeng M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 820–830.
• Recognition Method of Important Words in Korean Text based on Reinforcement Learning. Yang, Feiyang; Zhao, Yahui; Cui, Rongyi M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 1017–1025.
• 面向人工智能伦理计算的中文道德词典构建方法研究(Construction of a Chinese Moral Dictionary for Artificial Intelligence Ethical Computing). Wang, Hongrui; Liu, Chang; Yu, Dong M. Sun, S. Li, Y. Zhang, Y. Liu (eds.) (2020). 539–549.