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Kyle Richardson

Research Scientist

Allen Institute for Artificial Intelligence

Biography

I am a research scientist at the Allen Institute for Artificial Intelligence in Seattle, Washington where I do work on natural language processing and machine learning on the Aristo Project. Prior to this, I was a researcher at the Institute for Natural Language Processing (IMS) at the University of Stuttgart in Germany, where I received my PhD in October 2018. Before this, I received my B.A. from the University of Rochester in upstate New York (USA).

Teaching/Talks

In the summer of 2016, I taught a Masters seminar on Semantic Parsing (my thesis topic) at the University of Stuttgart. The slides and course materials can be found here.

Some recent notes and musings: Number Theory Meets Computability Theory (see also blog post); other lecture notes: Introduction to Probability

Recent Talks (see also below): Brief (10 minute) introduction to Natural Language Understanding (NLU) and Language Modeling (intended for a non-technical audience); Overview of my recent work on diagnostic testing of neural models; Text Modular Networks (collaborative work); Modeling Formal Fallacies (collaborative work,big-bench contribution); Temporal Reasoning on Implicit Events from Distance Supervision (collaborative work)

Recent Posts

I recently starting converting some of my research notes into blog posts, with the hope that someone might find them useful (or, even better, that someone might correct me when I’m wrong, since many of the topics covered go outside of my area of expertise).

Number Theory Meets Computability Theory

Solving Equations In this article1, we consider the problem of solving certain types of equations (called polynomial equations). For …

Why Infinity is Strange

What is Kolmogorov Complexity?

Selected Publications

Note: For the most up-to-date versions of my papers, please refer to the arxiv versions (unless stated otherwise).

Tushar Khot, Kyle Richardson , Daniel Khashabi, Ashish Sabharwal (2021) Learning to Solve Complex Tasks by Talking to Agents (work in progress) [arxiv] [code/data]

Gregor Betz, Kyle Richardson. (2021) DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models work in progress [arxiv]

Hai Hu, He Zhou, Zuoyu Tian, Yiwen Zhang, Yina Patterson, Yanting Li, Yixin Nie, Kyle Richardson. (2021) Investigating Transfer Learning in Multi-lingual Pre-trained Language Models through Chinese Natural Language Inference Findings of ACL [code/data] [arxiv] [acl anthology]

Gregor Betz, Christian Voigt, Kyle Richardson. (2021) Thinking Aloud: Dynamic Context Generation Improves Zero-Shot Reasoning Performance of GPT-2 work in progress [arxiv]

Ben Zhou, Kyle Richardson, Qiang Ning, Tushar Khot, Ashish Sabharwal, Dan Roth. (2021) Temporal Reasoning on Implicit Events from Distant Supervision Proceedings of the 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021) [arxiv] [code] [data] [leaderboard] [slides]

Tushar Khot, Daniel Khashabi, Kyle Richardson, Peter Clark, Ashish Sabharwal (2021) Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models Proceedings of the 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021) [arxiv] [code/data] [demo] [slides] [poster]

Gregor Betz, Christian Voigt, Kyle Richardson. (2021) Critical Thinking for Language Models Proceedings of International Conference on Computational Semantics (IWCS 2021) [arxiv] [data] [models] [blog] [proceedings] [video]

Sumithra Bhakthavatsalam, Daniel Khashabi, Tushar Khot, Bhavana Dalvi Mishra, Kyle Richardson, Ashish Sabharwal, Carissa Schoenick, Oyvind Tafjord, Peter Clark (2021) Think you have Solved Direct-Answer Question Answering? Try ARC-DA, the Direct-Answer AI2 Reasoning Challenge technical note [arxiv] [data]

Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, and Zhenzhong Lan. (2020) CLUE: A Chinese Language Understanding Evaluation Benchmark. in Proceedings of International Conference on Computational Linguistics (COLING) [arxiv] [website/leaderboard] [code/data] [proceedings]

Niket Tandon, Keisuke Sakaguchi, Bhavana Dalvi, Dheeraj Rajagopal, Peter Clark, Michal Guerquin, Kyle Richardson and Eduard Hovy. (2020) A Dataset for Tracking Entities in Open Domain Procedural Text in Proceedings of International Conference on Empirical Methods in Natural Language Processing (EMNLP) [proceedings] [arxiv] [dataset] [code]

Hai Hu, Kyle Richardson, Liang Xu, Lu Li, Sandra Kubler, Lawrence S. Moss. (2020) OCNLI: Original Chinese Natural Language Inference Findings of EMNLP [arxiv] [code/data] [leaderboard] [acl_anthonology]

Sumithra Bhakthavatsalam, Kyle Richardson, Niket Tandon, Peter Clark (2020) Do Dogs have Whiskers? A New Knowledge Base of hasPart Relations technical note [arxiv] [data]

Atticus Geiger, Kyle Richardson, Christopher Potts (2020) Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and Negation in Workshop on Analzying and Interpreting Neural Networks for NLP (BlackBoxNLP) [arxiv] [proceedings] [data]

Kyle Richardson, Ashish Sabharwal (2020). What Does My QA Model Know? Devising Controlled Probes using Expert Knowledge. in Transactions of the Association for Computational Linguistics (TACL) [arxiv] [journal] [code/data][slides (EMNLP2020)]

Peter Clark, Oyvind Tafjord,Kyle Richardson (2020). Transformers as Soft Reasoners over Language. Proceedings of International Joint Conference on Artificial Intelligence (IJCAI) [arxiv] [proceedings] [demo][data] [data generator code]

Hai Hu, Qi Chen, Kyle Richardson, Atreyee Mukherjee, Lawrence S. Moss,Sandra Kuebler (2020). MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity. Proceedings of Society for Computation in Linguistics (SCIL 2020) [arxiv] [proceedings] [data]

Kyle Richardson, Hai Hu, Lawrence S. Moss, Ashish Sabharwal (2020). Probing Natural Language Inference Models through Semantic Fragments. Proceedings of Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI) [arxiv][aaai][code/data][slides]

Peter Clark,Oren Etzioni, Daniel Khashabi, Tushar Khot, Bhavana Dalvi Mishra, Kyle Richardson, Ashish Sabharwal, Carissa Schoenick, Oyvind Tafjord, Niket Tandon, Sumithra Bhakthavatsalam, Dirk Groeneveld,Michal Guerquin, Michael Schmitz (2020). From ‘F’ to ‘A’ on the N.Y. Regents Science Exams: An Overview of the Aristo Project AI Magazine (to appear)[arxiv][New York Times, GeekWire]

Kyle Richardson (2018) New Resources and Ideas for Semantic Parser Induction. PhD Thesis, Institute for Natural Language Processing (IMS), Faculty of Computer Science, Electrical Engineering and Information Technology. University of Stuttgart, Germany [opus][slides][code/data][handout]

Kyle Richardson (2018) A Language for Function Signature Representations. Brief technical note. [arxiv][data]

Kyle Richardson, Jonathan Berant and Jonas Kuhn (2018). Polyglot Semantic Parsing in APIs. Proceedings of 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) [arxiv][data][notes][code][slides][video]

Kyle Richardson, Sina Zarrieß and Jonas Kuhn (2017). The Code2Text Challenge: Text Generation in Source Code Libraries (2017) Proceedings of International Natural Language Generation Conference (INLG) [arxiv][paper][inlg_slides][resources].

Kyle Richardson, Jonas Kuhn (2017). Function Assistant: A Tool for NL Querying of APIs. (2017) Proceedings of Empirical Methods in Natural Language Processing (EMNLP) [arxiv][paper][demo][resources][code][poster]

Kyle Richardson, Jonas Kuhn (2017). Learning Semantic Correspondences in Technical Documentation. Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL) [arvix][paper][notes][data][acl_poster][stuttgart slides][code].

Kyle Richardson, Jonas Kuhn. (2016) Learning to Make Inferences in a Semantic Parsing Task. Transactions of the Association for Computational Linguistics (TACL) [paper][data][acl_slides][video] [extended version (from thesis)].

Cleo Condoravdi, Kyle Richardson, Vishal Sikka, Asuman Suenbuel, and Richard Waldinger (2015) Natural Language Access to Data: It Takes Common Sense!. in Twelfth International Symposium on Logical Formalizations of Commonsense Reasoning (Commonsense-15). AAAI Spring Symposium. [demo][link]

Cleo Condoravdi, Kyle Richardson, Vishal Sikka, Asuman Suenbuel, and Richard Waldinger (2014) Deduction for Natural Language Access to Data. in University of Coimbra CS Technical Reports, CISUC/TR 2014-02. Presented at Joint Workshop on Natural Language and Computer Science (NLCS) and Natural Language Services for Reasoners (NLSR).

Kyle Richardson and Jonas Kuhn (2014) UnixMan Corpus: A Resource for Language Learning in the Unix Domain. in Proceedings of Language Resources and Evaluation (LREC). [link] [data]

Sina Zarriess and Kyle Richardson. (2013) An Automatic Method for Building a Data-to-Text Generator. in Proceedings of 14th European Workshop on Natural Language Generation (ENLG) [link]

Richard Waldinger, Danny Bobrow, Cleo Condoravdi, Amar Das, Kyle Richardson. (2011) Accessing Structured Health Information through English Queries and Automatic Deduction. in Proceedings of AAAI Spring Symposium on Health Communications.