200 citations · 461 across the 46 of their papers we have counts for
6 papers · 1 filter
MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining
Zhi Wen, Xing Han Lu, Siva Reddy
One of the biggest challenges that prohibit the use of many current NLP methods in clinical settings is the availability of public datasets. In this work, we present MeDAL, a large…
Explicitly Modeling Syntax in Language Models with Incremental Parsing and a Dynamic Oracle
Yikang Shen, Shawn Tan, Alessandro Sordoni +2
Syntax is fundamental to our thinking about language. Failing to capture the structure of input language could lead to generalization problems and over-parametrization. In the pres…
Measuring Systematic Generalization in Neural Proof Generation with Transformers
Nicolas Gontier, Koustuv Sinha, Siva Reddy +1
We are interested in understanding how well Transformer language models (TLMs) can perform reasoning tasks when trained on knowledge encoded in the form of natural language. We inv…
Learning Improvised Chatbots from Adversarial Modifications of Natural Language Feedback
Makesh Narsimhan Sreedhar, Kun Ni, Siva Reddy
The ubiquitous nature of chatbots and their interaction with users generate an enormous amount of data. Can we improve chatbots using this data? A self-feeding chatbot improves its…
Words aren't enough, their order matters: On the Robustness of Grounding Visual Referring Expressions
Arjun R Akula, Spandana Gella, Yaser Al-Onaizan +2
Visual referring expression recognition is a challenging task that requires natural language understanding in the context of an image. We critically examine RefCOCOg, a standard be…
StereoSet: Measuring stereotypical bias in pretrained language models
Moin Nadeem, Anna Bethke, Siva Reddy
A stereotype is an over-generalized belief about a particular group of people, e.g., Asians are good at math or Asians are bad drivers. Such beliefs (biases) are known to hurt targ…