Pradeep Dasigi

4 paper cards in Mova's library · 2017–2020

Pradeep Dasigi wrote 4 of the papers Mova reads, in 2017–2020; often with Matt Gardner, Dheeru Dua, Nelson F. Liu.

The profile is the library's match of the name; two people with one name are not always told apart.

Papers

Evaluating Models' Local Decision Boundaries via Contrast Sets (2020) measured
Standard test sets come from the same distribution as the training data, so a dataset with systematic gaps or annotation artifacts lets models score well with shallow heuristics.
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs (2019)
DROP has 96,567 questions on about 7,000 Wikipedia passages rich in numbers (American-football game summaries, history), written adversarially against a reading model so that answering needs discrete operations over several places in the text.
Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning (2019)
Quoref has over 24K span-answer questions on 4.7K Wikipedia paragraphs (about 40% film plot summaries, which mention many characters), written by crowd workers so that answering needs linking two mentions of one entity.
Ontology-Aware Token Embeddings for Prepositional Phrase Attachment (2017)
A type-level embedding gives one vector per word form, so "pool" as a place and "pool" as a game share a vector.