Papers
The Meaning of “Most” for Visual Question Answering Models
Alexander Kuhnle, Ann Copestake
Multi-Granular Text Encoding for Self-Explaining Categorization
Zhiguo Wang, Yue Zhang, Mo Yu et al.
Can Neural Networks Understand Monotonicity Reasoning?
Hitomi Yanaka, Koji Mineshima, Daisuke Bekki et al.
Second-order Co-occurrence Sensitivity of Skip-Gram with Negative Sampling
Dominik Schlechtweg, Cennet Oguz, Sabine Schulte im Walde
Sentiment Analysis Is Not Solved! Assessing and Probing Sentiment Classification
Jeremy Barnes, Lilja Øvrelid, Erik Velldal
Transcoding Compositionally: Using Attention to Find More Generalizable Solutions
Kris Korrel, Dieuwke Hupkes, Verna Dankers et al.
Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP
Tal Linzen, Grzegorz Chrupała, Yonatan Belinkov et al.
ARS_NITK at MEDIQA 2019:Analysing Various Methods for Natural Language Inference, Recognising Question Entailment and Medical Question Answering System
Anumeha Agrawal, Rosa Anil George, Selvan Suntiha Ravi et al.
NCUEE at MEDIQA 2019: Medical Text Inference Using Ensemble BERT-BiLSTM-Attention Model
Lung-Hao Lee, Yi Lu, Po-Han Chen et al.
LasigeBioTM at MEDIQA 2019: Biomedical Question Answering using Bidirectional Transformers and Named Entity Recognition
Andre Lamurias, Francisco M Couto
IITP at MEDIQA 2019: Systems Report for Natural Language Inference, Question Entailment and Question Answering
Dibyanayan Bandyopadhyay, Baban Gain, Tanik Saikh et al.
Saama Research at MEDIQA 2019: Pre-trained BioBERT with Attention Visualisation for Medical Natural Language Inference
Kamal raj Kanakarajan, Suriyadeepan Ramamoorthy, Vaidheeswaran Archana et al.
UW-BHI at MEDIQA 2019: An Analysis of Representation Methods for Medical Natural Language Inference
William Kearns, Wilson Lau, Jason Thomas
UU_TAILS at MEDIQA 2019: Learning Textual Entailment in the Medical Domain
Noha Tawfik, Marco Spruit
MSIT_SRIB at MEDIQA 2019: Knowledge Directed Multi-task Framework for Natural Language Inference in Clinical Domain.
Sahil Chopra, Ankita Gupta, Anupama Kaushik
ANU-CSIRO at MEDIQA 2019: Question Answering Using Deep Contextual Knowledge
Vincent Nguyen, Sarvnaz Karimi, Zhenchang Xing
IIT-KGP at MEDIQA 2019: Recognizing Question Entailment using Sci-BERT stacked with a Gradient Boosting Classifier
Prakhar Sharma, Sumegh Roychowdhury
Sieg at MEDIQA 2019: Multi-task Neural Ensemble for Biomedical Inference and Entailment
Sai Abishek Bhaskar, Rashi Rungta, James Route et al.
Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations
Vinayshekhar Bannihatti Kumar, Ashwin Srinivasan, Aditi Chaudhary et al.
DUT-BIM at MEDIQA 2019: Utilizing Transformer Network and Medical Domain-Specific Contextualized Representations for Question Answering
Huiwei Zhou, Bizun Lei, Zhe Liu et al.
DUT-NLP at MEDIQA 2019: An Adversarial Multi-Task Network to Jointly Model Recognizing Question Entailment and Question Answering
Huiwei Zhou, Xuefei Li, Weihong Yao et al.
KU_ai at MEDIQA 2019: Domain-specific Pre-training and Transfer Learning for Medical NLI
Cemil Cengiz, Ulaş Sert, Deniz Yuret
WTMED at MEDIQA 2019: A Hybrid Approach to Biomedical Natural Language Inference
Zhaofeng Wu, Yan Song, Sicong Huang et al.
Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model
Jiin Nam, Seunghyun Yoon, Kyomin Jung
DoubleTransfer at MEDIQA 2019: Multi-Source Transfer Learning for Natural Language Understanding in the Medical Domain
Yichong Xu, Xiaodong Liu, Chunyuan Li et al.