Search Results for author: Ananth Gottumukkala

Found 5 papers, 1 papers with code

Evaluating NLP Models via Contrast Sets

no code implementations1 Oct 2020 Matt Gardner, Yoav Artzi, Victoria Basmova, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hanna Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, A. Zhang, Ben Zhou

Unfortunately, when a dataset has systematic gaps (e. g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture a dataset's intended capabilities.

Reading Comprehension Sentiment Analysis

Dynamic Sampling Strategies for Multi-Task Reading Comprehension

no code implementations ACL 2020 Ananth Gottumukkala, Dheeru Dua, Sameer Singh, Matt Gardner

Building general reading comprehension systems, capable of solving multiple datasets at the same time, is a recent aspirational goal in the research community.

Multi-Task Learning Reading Comprehension

ORB: An Open Reading Benchmark for Comprehensive Evaluation of Machine Reading Comprehension

no code implementations29 Dec 2019 Dheeru Dua, Ananth Gottumukkala, Alon Talmor, Sameer Singh, Matt Gardner

A lot of diverse reading comprehension datasets have recently been introduced to study various phenomena in natural language, ranging from simple paraphrase matching and entity typing to entity tracking and understanding the implications of the context.

Entity Typing Machine Reading Comprehension +2

Comprehensive Multi-Dataset Evaluation of Reading Comprehension

no code implementations WS 2019 Dheeru Dua, Ananth Gottumukkala, Alon Talmor, Sameer Singh, Matt Gardner

A lot of diverse reading comprehension datasets have recently been introduced to study various phenomena in natural language, ranging from simple paraphrase matching and entity typing to entity tracking and understanding the implications of the context.

Entity Typing Natural Language Understanding +2

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