Kyle Lo

Allen Institute for Artificial Intelligence
Publications35
h-index15
Citations2,259
Highly Influential Citations376
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SciBERT: A Pretrained Language Model for Scientific Text
TLDR
SciBERT leverages unsupervised pretraining on a large multi-domain corpus of scientific publications to improve performance on downstream scientific NLP tasks and demonstrates statistically significant improvements over BERT. Expand
Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks
TLDR
It is consistently found that multi-phase adaptive pretraining offers large gains in task performance, and it is shown that adapting to a task corpus augmented using simple data selection strategies is an effective alternative, especially when resources for domain-adaptive pretraining might be unavailable. Expand
CORD-19: The COVID-19 Open Research Dataset
TLDR
The mechanics of dataset construction are described, highlighting challenges and key design decisions, an overview of how CORD-19 has been used, and several shared tasks built around the dataset are described. Expand
SciBERT: Pretrained Contextualized Embeddings for Scientific Text
TLDR
SciBERT leverages unsupervised pretraining on a large multi-domain corpus of scientific publications to improve performance on downstream scientific NLP tasks and demonstrates statistically significant improvements over BERT. Expand
Construction of the Literature Graph in Semantic Scholar
TLDR
This paper reduces literature graph construction into familiar NLP tasks, point out research challenges due to differences from standard formulations of these tasks, and report empirical results for each task. Expand
S2ORC: The Semantic Scholar Open Research Corpus
TLDR
In S2ORC, a large corpus of 81.1M English-language academic papers spanning many academic disciplines is introduced, which is expected to facilitate research and development of tools and tasks for text mining over academic text. Expand
TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection
TREC-COVID is a community evaluation designed to build a test collection that captures the information needs of biomedical researchers using the scientific literature during a pandemic. One of the… Expand
Fact or Fiction: Verifying Scientific Claims
We introduce scientific claim verification, a new task to select abstracts from the research literature containing evidence that supports or refutes a given scientific claim, and to identify… Expand
TLDR: Extreme Summarization of Scientific Documents
TLDR
This work introduces SCITLDR, a new multi-target dataset of 5.4K TLDRs over 3.2K papers, and proposes CATTS, a simple yet effective learning strategy for generatingTLDRs that exploits titles as an auxiliary training signal. Expand
TREC-COVID: rationale and structure of an information retrieval shared task for COVID-19
TLDR
TREC-COVID differs from traditional IR shared task evaluations with special considerations for the expected users, IR modality considerations, topic development, participant requirements, assessment process, relevance criteria, evaluation metrics, iteration process, projected timeline, and the implications of data use as a post-task test collection. Expand
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