Accepted papers (oral)

  • DRAGON: Deep Bidirectional Language-Knowledge Graph Pretraining. Michihiro Yasunaga (Stanford University); Antoine Bosselut (EPFL); Hongyu Ren (Stanford University); Xikun Zhang (Stanford University); Christopher D Manning (Stanford University); Percy Liang (Stanford University); Jure Leskovec (Stanford University) [Link]
  • ComFact: A Benchmark for Linking Contextual Commonsense Knowledge. Silin Gao (EPFL); Jena D Hwang (Allen Institute for AI); Saya Kanno (Sony Group Corporation); Hiromi Wakaki (Sony Group Corporation); Yuki Mitsufuji (Sony Group Corporation); Antoine Bosselut (EPFL) [Link]
  • Knowledge Retrieval Over Public and Private Data. Simran Arora (Stanford University); Patrick Lewis (Meta); Angela Fan (Facebook AI Research); Jacob D Kahn (Facebook AI Research); Christopher Re (Stanford University) [Link]
  • Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering. Pan Lu (UCLA); Swaroop Ranjan Mishra (ASU); Tony Xia (UCLA); Liang Qiu (UCLA); Kai-Wei Chang (UCLA); Song-Chun Zhu (UCLA); Oyvind Tafjord (AI2); Peter Clark (Allen Institute for AI); Ashwin Kalyan (AI2) [Link]
  • Knowledge Relevance BERT: Integrating Noisy Knowledge into Language Representation. Karan Samel (Georgia Tech); Jun Ma (Amazon); Zhengyang Wang (Amazon); Tong Zhao (Uber); Irfan Essa (Georgia Institute of Technology) [Link]
  • Improving Mental Health Support Response Generation with Eventuality-based Knowledge Graph. Lingbo Tong (University of Notre Dame); Qi Liu (University of Washington); Wenhao Yu (University of Notre Dame); Mengxia Yu (University of Notre Dame); Zhihan Zhang (University of Notre Dame); Meng Jiang (University of Notre Dame) [Link]

Accepted papers (poster)

  • Link-BERT: Pretraining a Language Model with Document Links. Michihiro Yasunaga (Stanford University); Jure Leskovec (Stanford University); Percy Liang (Stanford University) [Link]
  • Penalizing Confident Predictions on Largely Perturbed Inputs Does Not Improve Out-of-Distribution Generalization in Question Answering. Kazutoshi Shinoda (The University of Tokyo); Saku Sugawara (National Institute of Informatics); Akiko Aizawa (National Institute of Informatics) [Link]
  • Can Open-Domain QA Reader Utilize External Knowledge Efficiently like Humans? Neeraj Varshney (Arizona State University); Man Luo (Arizona State University); Chitta Baral (Arizona State University) [Link]
  • Weakly Supervised Explainable Phrasal Reasoning with Neural Fuzzy Logic. Zijun Wu (University of Alberta); Zixuan Zhang (University of Alberta); Atharva Roshan Naik (Carnegie Mellon University); Zhijian Mei (University of Alberta); Mauajama Firdaus (University of Alberta); Lili Mou (University of Alberta) [Link]
  • Can a Frozen Pretrained Language Model be used for Zero-shot Neural Retrieval on Entity-centric Questions? Yasuto Hoshi (Kioxia Corporation); Daisuke Miyashita (Kioxia Corporation); Yasuhiro Morioka (KIOXIA Corporation); Youyang Ng (Kioxia Corporation); Osamu Torii (Kioxia Corporation); Jun Deguchi (Kioxia Corporation) [Link]
  • Commonsense-Aware Prompting for Controllable Empathetic Dialogue Generation. Yiren Liu (University of Illinois at Urbana-Champaign); Halil Kilicoglu (University of Illinois at Urbana-Champaign) [Link]
  • Pragmatic Constraint on Distributional Semantics. Elizaveta Zhemchuzhina (Yandex and Higher School of Economics); Nikolay N Filippov (Yandex, Higher School of Economics); Ivan P Yamshchikov (Cemapre University of Lisbon) [Link]
  • Contrastive Learning Approach to Word-in-Context Task Considering Low-Resource Languages. Pei-Chi Lo (Singapore Management University); Ee-peng Lim (Singapore Management University) [Link]
  • KAER: A Knowledge Augmented Pre-Trained Language Model for Entity Resolution. Liri Fang (University of Illinois Urbana-Champaign); Lan Li (UIUC); Yiren Liu (University of Illinois at Urbana-Champaign); Vetle I Torvik (University of Illinois at Urbana-Champaign); Bertram Ludaescher (University of Illinois) [Link]
  • Efficient and effective training of language and graph neural network models. Vassilis N. Ioannidis (Amazon Web Services); Da Zheng (Amazon); Xiang Song (Amazon); Houyu Zhang (Amazon); Jun Ma (Amazon); Yi Xu (Amazon); Belinda Zeng (Amazon); Trishul A Chilimbi (Amazon); George Karypis (Amazon) [Link]
  • StoryQA: Story Grounded Question Answering Dataset. Sanqiang Zhao (Amazon Alexa AI); Seokhwan Kim (Amazon Alexa AI); Yang Liu (Amazon, Alexa AI); Robinson Piramuthu (Amazon Inc); Dilek Z Hakkani-Tur (Amazon Alexa AI) [Link]
  • Formal-Logical Distributional Semantics: Applications to Property Inference. Michael J Sullivan (University at Buffalo) [Link]
  • Distributional Data Augmentation Methods for Low Resource Language. Mosleh Mahamud (Stockholm University); Zed Lee (Stockholm University); Isak Samsten (Stockholm University) [Link]
  • Commonsense Reasoning for Conversational AI: A Survey of the State of the Art. Christopher G Richardson (Georgia Institute of Technology); Larry Heck (Georgia Institute of Technology) [Link]
  • Topic Aware Text Generation From Given KGs. Noriaki Kawamae (NTT Comware) [Link]
  • ArgAnalysis35K: A large-scale dataset for Argument Quality Analysis. Omkar J Joshi (College of Engineering, Pune); Priya N Pitre (College of Engineering, Pune); Dr. Mrs. Yashodhara V. Haribhakta (College of Engineering Pune) [Link]
  • Utilizing Background Knowledge for Robust Reasoning over Traffic Situations. Jiarui Zhang (USC); Filip Ilievski (USC); Aravinda Kolla (USC); Jonathan Francis (Bosch Research Pittsburgh); Kaixin Ma (CMU); Alessandro Oltramari (Bosch Research Pittsburgh) [Link]
  • CECADA: Cause-Effect Conjunctive Adverb-based Data Augmentation Method in Low-Resource Knowledge-Grounded Dialogue. Taesuk Hong (Sogang University, LG AI Research); Dongkyu Lee (Hong Kong University of Science and Technology); Janghoon Han (LG AI Research); Stanley Jungkyu Choi (LG AI Research); Jungyun Seo (Sogang University) [Link]
  • Template-augmented Dialogue Representation Learning. Minsik Oh (Amazon); Guoyin Wang (Amazon); Taiwoo Park (Amazon); Puyang Xu (Amazon) [Link]
  • Timeline as a Knowledge Representation for Retrieving Similar Safety Incidents from Industrial Repositories. Sangameshwar Patil (TCS Research); Nitin Ramrakhiyani (TCS Research); Swapnil Hingmire; Alok Kumar (TCS Research); Girish Keshav Palshikar (Tata Consultancy Services); Harsimran Bedi (TCS Research) [Link]