activity
19942023
most citedUnsupervised Data Augmentation for Consistency Training

1.6k citations · 3.9k across the 96 of their papers we have counts for

collaborators

105 papers

cs.CL2023

What's the Meaning of Superhuman Performance in Today's NLU?

Simone Tedeschi, Johan Bos, Thierry Declerck +9

In the last five years, there has been a significant focus in Natural Language Processing (NLP) on developing larger Pretrained Language Models (PLMs) and introducing benchmarks su…

cs.CL2023

Data-efficient Active Learning for Structured Prediction with Partial Annotation and Self-Training

Zhisong Zhang, Emma Strubell, Eduard Hovy

In this work we propose a pragmatic method that reduces the annotation cost for structured label spaces using active learning. Our approach leverages partial annotation, which redu…

cs.CL2023

Summarizing Multiple Documents with Conversational Structure for Meta-Review Generation

Miao Li, Eduard Hovy, Jey Han Lau

We present PeerSum, a novel dataset for generating meta-reviews of scientific papers. The meta-reviews can be interpreted as abstractive summaries of reviews, multi-turn discussion…

cs.CL2022★ 1 cited

EvEntS ReaLM: Event Reasoning of Entity States via Language Models

Evangelia Spiliopoulou, Artidoro Pagnoni, Yonatan Bisk +1

This paper investigates models of event implications. Specifically, how well models predict entity state-changes, by targeting their understanding of physical attributes. Nominally…

cs.CL2022★ 9 cited

A Survey of Active Learning for Natural Language Processing

Zhisong Zhang, Emma Strubell, Eduard Hovy

In this work, we provide a survey of active learning (AL) for its applications in natural language processing (NLP). In addition to a fine-grained categorization of query strategie…

cs.CL2022★ 3 cited

CHARD: Clinical Health-Aware Reasoning Across Dimensions for Text Generation Models

Steven Y. Feng, Vivek Khetan, Bogdan Sacaleanu +2

We motivate and introduce CHARD: Clinical Health-Aware Reasoning across Dimensions, to investigate the capability of text generation models to act as implicit clinical knowledge ba…