activity
20162022
most citedTowards modular and programmable architecture search

11 citations · 15 across the 6 of their papers we have counts for

collaborators

12 papers

cs.CL20222 cited

Revisiting text decomposition methods for NLI-based factuality scoring of summaries

John Glover, Federico Fancellu, Vasudevan Jagannathan +2

Scoring the factuality of a generated summary involves measuring the degree to which a target text contains factual information using the input document as support. Given the simil…

cs.CL2022

He Said, She Said: Style Transfer for Shifting the Perspective of Dialogues

Amanda Bertsch, Graham Neubig, Matthew R. Gormley

In this work, we define a new style transfer task: perspective shift, which reframes a dialogue from informal first person to a formal third person rephrasing of the text. This tas…

cs.CL20221 cited

On Efficiently Acquiring Annotations for Multilingual Models

Joel Ruben Antony Moniz, Barun Patra, Matthew R. Gormley

When tasked with supporting multiple languages for a given problem, two approaches have arisen: training a model for each language with the annotation budget divided equally among…

cs.CL20211 cited

Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversations

Longxiang Zhang, Renato Negrinho, Arindam Ghosh +4

Fine-tuning pretrained models for automatically summarizing doctor-patient conversation transcripts presents many challenges: limited training data, significant domain shift, long…

cs.CL2021

Comparative Error Analysis in Neural and Finite-state Models for Unsupervised Character-level Transduction

Maria Ryskina, Eduard Hovy, Taylor Berg-Kirkpatrick +1

Traditionally, character-level transduction problems have been solved with finite-state models designed to encode structural and linguistic knowledge of the underlying process, whe…

cs.CL2020

An Empirical Investigation of Beam-Aware Training in Supertagging

Renato Negrinho, Matthew R. Gormley, Geoffrey J. Gordon

Structured prediction is often approached by training a locally normalized model with maximum likelihood and decoding approximately with beam search. This approach leads to mismatc…