activity
20172021
most citedWell-Read Students Learn Better: On the Importance of Pre-training Compact Models

428 citations · 858 across the 8 of their papers we have counts for

collaborators

14 papers

cs.CL202137 cited

Revisiting the Primacy of English in Zero-shot Cross-lingual Transfer

Iulia Turc, Kenton Lee, Jacob Eisenstein +2

Despite their success, large pre-trained multilingual models have not completely alleviated the need for labeled data, which is cumbersome to collect for all target languages. Zero…

cs.CL2021

Joint Passage Ranking for Diverse Multi-Answer Retrieval

Sewon Min, Kenton Lee, Ming-Wei Chang +2

We study multi-answer retrieval, an under-explored problem that requires retrieving passages to cover multiple distinct answers for a given question. This task requires joint model…

cs.CL2021

Representations for Question Answering from Documents with Tables and Text

Vicky Zayats, Kristina Toutanova, Mari Ostendorf

Tables in Web documents are pervasive and can be directly used to answer many of the queries searched on the Web, motivating their integration in question answering. Very often inf…

cs.CL202013 cited

Probabilistic Assumptions Matter: Improved Models for Distantly-Supervised Document-Level Question Answering

Hao Cheng, Ming-Wei Chang, Kenton Lee +1

We address the problem of extractive question answering using document-level distant super-vision, pairing questions and relevant documents with answer strings. We compare previous…

cs.CL2020

Sparse, Dense, and Attentional Representations for Text Retrieval

Yi Luan, Jacob Eisenstein, Kristina Toutanova +1

Dual encoders perform retrieval by encoding documents and queries into dense lowdimensional vectors, scoring each document by its inner product with the query. We investigate the c…

cs.CL2019428 cited

Well-Read Students Learn Better: On the Importance of Pre-training Compact Models

Iulia Turc, Ming-Wei Chang, Kenton Lee +1

Recent developments in natural language representations have been accompanied by large and expensive models that leverage vast amounts of general-domain text through self-supervise…