150 citations · 367 across the 8 of their papers we have counts for
10 papers
Selectively Answering Ambiguous Questions
Jeremy R. Cole, Michael J. Q. Zhang, Daniel Gillick +3
Trustworthy language models should abstain from answering questions when they do not know the answer. However, the answer to a question can be unknown for a variety of reasons. Pri…
NAIL: Lexical Retrieval Indices with Efficient Non-Autoregressive Decoders
Livio Baldini Soares, Daniel Gillick, Jeremy R. Cole +1
Neural document rerankers are extremely effective in terms of accuracy. However, the best models require dedicated hardware for serving, which is costly and often not feasible. To…
Time-Aware Language Models as Temporal Knowledge Bases
Bhuwan Dhingra, Jeremy R. Cole, Julian Martin Eisenschlos +3
Many facts come with an expiration date, from the name of the President to the basketball team Lebron James plays for. But language models (LMs) are trained on snapshots of data co…
MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network
Nicholas FitzGerald, Jan A. Botha, Daniel Gillick +3
We present an instance-based nearest neighbor approach to entity linking. In contrast to most prior entity retrieval systems which represent each entity with a single vector, we bu…
Entity Linking in 100 Languages
Jan A. Botha, Zifei Shan, Daniel Gillick
We propose a new formulation for multilingual entity linking, where language-specific mentions resolve to a language-agnostic Knowledge Base. We train a dual encoder in this new se…
Learning Dense Representations for Entity Retrieval
Daniel Gillick, Sayali Kulkarni, Larry Lansing +4
We show that it is feasible to perform entity linking by training a dual encoder (two-tower) model that encodes mentions and entities in the same dense vector space, where candidat…