101 citations · 162 across the 5 of their papers we have counts for
6 papers · 1 filter
Improving Factual Consistency in Summarization with Compression-Based Post-Editing
Alexander R. Fabbri, Prafulla Kumar Choubey, Jesse Vig +2
State-of-the-art summarization models still struggle to be factually consistent with the input text. A model-agnostic way to address this problem is post-editing the generated summ…
SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization
Jesse Vig, Wojciech Kryściński, Karan Goel +1
Novel neural architectures, training strategies, and the availability of large-scale corpora haven been the driving force behind recent progress in abstractive text summarization.…
Robustness Gym: Unifying the NLP Evaluation Landscape
Karan Goel, Nazneen Rajani, Jesse Vig +6
Despite impressive performance on standard benchmarks, deep neural networks are often brittle when deployed in real-world systems. Consequently, recent research has focused on test…
BERTology Meets Biology: Interpreting Attention in Protein Language Models
Jesse Vig, Ali Madani, Lav R. Varshney +3
Transformer architectures have proven to learn useful representations for protein classification and generation tasks. However, these representations present challenges in interpre…
Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias
Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov +6
Common methods for interpreting neural models in natural language processing typically examine either their structure or their behavior, but not both. We propose a methodology grou…
Analyzing the Structure of Attention in a Transformer Language Model
Jesse Vig, Yonatan Belinkov
The Transformer is a fully attention-based alternative to recurrent networks that has achieved state-of-the-art results across a range of NLP tasks. In this paper, we analyze the s…