45 citations · 102 across the 4 of their papers we have counts for
7 papers
Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language Modeling
Robert L. Logan, Nelson F. Liu, Matthew E. Peters +2
Modeling human language requires the ability to not only generate fluent text but also encode factual knowledge. However, traditional language models are only capable of rememberin…
Compositional Questions Do Not Necessitate Multi-hop Reasoning
Sewon Min, Eric Wallace, Sameer Singh +3
Multi-hop reading comprehension (RC) questions are challenging because they require reading and reasoning over multiple paragraphs. We argue that it can be difficult to construct l…
Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing
Ben Bogin, Matt Gardner, Jonathan Berant
Research on parsing language to SQL has largely ignored the structure of the database (DB) schema, either because the DB was very simple, or because it was observed at both trainin…
Grammar-based Neural Text-to-SQL Generation
Kevin Lin, Ben Bogin, Mark Neumann +2
The sequence-to-sequence paradigm employed by neural text-to-SQL models typically performs token-level decoding and does not consider generating SQL hierarchically from a grammar.…
Linguistic Knowledge and Transferability of Contextual Representations
Nelson F. Liu, Matt Gardner, Yonatan Belinkov +2
Contextual word representations derived from large-scale neural language models are successful across a diverse set of NLP tasks, suggesting that they encode useful and transferabl…
Simple and Effective Multi-Paragraph Reading Comprehension
Christopher Clark, Matt Gardner
We consider the problem of adapting neural paragraph-level question answering models to the case where entire documents are given as input. Our proposed solution trains models to p…