Papers
Diversity-aware Event Prediction based on a Conditional Variational Autoencoder with Reconstruction
Hirokazu Kiyomaru, Kazumasa Omura, Yugo Murawaki et al.
Commonsense inference in human-robot communication
Aliaksandr Huminski, Yan Bin Ng, Kenneth Kwok et al.
Pingan Smart Health and SJTU at COIN - Shared Task: utilizing Pre-trained Language Models and Common-sense Knowledge in Machine Reading Tasks
Xiepeng Li, Zhexi Zhang, Wei Zhu et al.
IIT-KGP at COIN 2019: Using pre-trained Language Models for modeling Machine Comprehension
Prakhar Sharma, Sumegh Roychowdhury
Commonsense Inference in Natural Language Processing (COIN) - Shared Task Report
Simon Ostermann, Sheng Zhang, Michael Roth et al.
Extracting Common Inference Patterns from Semi-Structured Explanations
Sebastian Thiem, Peter Jansen
When Choosing Plausible Alternatives, Clever Hans can be Clever
Pride Kavumba, Naoya Inoue, Benjamin Heinzerling et al.
Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering
Kaixin Ma, Jonathan Francis, Quanyang Lu et al.
A Hybrid Neural Network Model for Commonsense Reasoning
Pengcheng He, Xiaodong Liu, Weizhu Chen et al.
Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing
Simon Ostermann, Sheng Zhang, Michael Roth et al.
CoSSAT: Code-Switched Speech Annotation Tool
Sanket Shah, Pratik Joshi, Sebastin Santy et al.
Computer Assisted Annotation of Tension Development in TED Talks through Crowdsourcing
Seungwon Yoon, Wonsuk Yang, Jong Park
Crowd-sourcing annotation of complex NLU tasks: A case study of argumentative content annotation
Tamar Lavee, Lili Kotlerman, Matan Orbach et al.
Leveraging syntactic parsing to improve event annotation matching
Camiel Colruyt, Orphée De Clercq, Véronique Hoste
Word Familiarity Rate Estimation Using a Bayesian Linear Mixed Model
Masayuki Asahara
Dependency Tree Annotation with Mechanical Turk
Stephen Tratz
An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering
Shayne Longpre, Yi Lu, Zhucheng Tu et al.
D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension
Hongyu Li, Xiyuan Zhang, Yibing Liu et al.