Bend but Don't Break? Multi-Challenge Stress Test for QA Models

no code implementations • WS 2019 • Hemant Pugaliya, James Route, Kaixin Ma, Yixuan Geng, Eric Nyberg

The field of question answering (QA) has seen rapid growth in new tasks and modeling approaches in recent years.

Question Answering

Multimodal, Multilingual Grapheme-to-Phoneme Conversion for Low-Resource Languages

no code implementations • WS 2019 • James Route, Steven Hillis, Isak Czeresnia Etinger, Han Zhang, Alan W. black

Grapheme-to-phoneme conversion (g2p) is the task of predicting the pronunciation of words from their orthographic representation.

Sieg at MEDIQA 2019: Multi-task Neural Ensemble for Biomedical Inference and Entailment

no code implementations • WS 2019 • Sai Abishek Bhaskar, Rashi Rungta, James Route, Eric Nyberg, Teruko Mitamura

This paper presents a multi-task learning approach to natural language inference (NLI) and question entailment (RQE) in the biomedical domain.

Multi-Task Learning Natural Language Inference +1

Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations

no code implementations • WS 2019 • Vinayshekhar Bannihatti Kumar, Ashwin Srinivasan, Aditi Chaudhary, James Route, Teruko Mitamura, Eric Nyberg

This paper presents the submissions by Team Dr. Quad to the ACL-BioNLP 2019 shared task on Textual Inference and Question Entailment in the Medical Domain.

Data Augmentation Natural Language Inference

Mining Data from the Congressional Record

no code implementations • 3 Jun 2019 • Zhengyu Ma, Tianjiao Qi, James Route, Amir Ziai

We propose a data storage and analysis method for using the US Congressional record as a policy analysis tool.