activity
20182025
most citedIn-context Examples Selection for Machine Translation

17 citations · 21 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CL202217 cited

In-context Examples Selection for Machine Translation

Sweta Agrawal, Chunting Zhou, Mike Lewis +2

Large-scale generative models show an impressive ability to perform a wide range of Natural Language Processing (NLP) tasks using in-context learning, where a few examples are used…

cs.CL2022

Controlling Translation Formality Using Pre-trained Multilingual Language Models

Elijah Rippeth, Sweta Agrawal, Marine Carpuat

This paper describes the University of Maryland's submission to the Special Task on Formality Control for Spoken Language Translation at \iwslt, which evaluates translation from En…

cs.CL2022

An Imitation Learning Curriculum for Text Editing with Non-Autoregressive Models

Sweta Agrawal, Marine Carpuat

We propose a framework for training non-autoregressive sequence-to-sequence models for editing tasks, where the original input sequence is iteratively edited to produce the output.…

cs.CL2021

Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer

Eleftheria Briakou, Sweta Agrawal, Joel Tetreault +1

While the field of style transfer (ST) has been growing rapidly, it has been hampered by a lack of standardized practices for automatic evaluation. In this paper, we evaluate leadi…

cs.CL2021

A Review of Human Evaluation for Style Transfer

Eleftheria Briakou, Sweta Agrawal, Ke Zhang +2

This paper reviews and summarizes human evaluation practices described in 97 style transfer papers with respect to three main evaluation aspects: style transfer, meaning preservati…

cs.CL2021

Assessing Reference-Free Peer Evaluation for Machine Translation

Sweta Agrawal, George Foster, Markus Freitag +1

Reference-free evaluation has the potential to make machine translation evaluation substantially more scalable, allowing us to pivot easily to new languages or domains. It has been…