Search Results for author: Sweta Agrawal

Found 9 papers, 4 papers with code

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

1 code implementation EMNLP 2021 Eleftheria Briakou, Sweta Agrawal, Joel Tetreault, Marine Carpuat

While the field of style transfer (ST) has been growing rapidly, it has been hampered by a lack of standardized practices for automatic evaluation.

Style Transfer

A Review of Human Evaluation for Style Transfer

1 code implementation ACL (GEM) 2021 Eleftheria Briakou, Sweta Agrawal, Ke Zhang, Joel Tetreault, Marine Carpuat

However, in style transfer papers, we find that protocols for human evaluations are often underspecified and not standardized, which hampers the reproducibility of research in this field and progress toward better human and automatic evaluation methods.

Style Transfer

Assessing Reference-Free Peer Evaluation for Machine Translation

no code implementations NAACL 2021 Sweta Agrawal, George Foster, Markus Freitag, Colin Cherry

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

Machine Translation Translation

Multitask Models for Controlling the Complexity of Neural Machine Translation

no code implementations WS 2020 Sweta Agrawal, Marine Carpuat

We introduce a machine translation task where the output is aimed at audiences of different levels of target language proficiency.

Machine Translation Translation

Generating Diverse Translations via Weighted Fine-tuning and Hypotheses Filtering for the Duolingo STAPLE Task

no code implementations WS 2020 Sweta Agrawal, Marine Carpuat

This paper describes the University of Maryland{'}s submission to the Duolingo Shared Task on Simultaneous Translation And Paraphrase for Language Education (STAPLE).

Fine-tuning Machine Translation +1

Controlling Text Complexity in Neural Machine Translation

1 code implementation IJCNLP 2019 Sweta Agrawal, Marine Carpuat

This work introduces a machine translation task where the output is aimed at audiences of different levels of target language proficiency.

Machine Translation Translation

Cannot find the paper you are looking for? You can Submit a new open access paper.