Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash 98 tok/s
Gemini 2.5 Pro 50 tok/s Pro
GPT-5 Medium 33 tok/s
GPT-5 High 29 tok/s Pro
GPT-4o 87 tok/s
GPT OSS 120B 465 tok/s Pro
Kimi K2 220 tok/s Pro
2000 character limit reached

Generating Diverse Translation with Perturbed kNN-MT (2402.09344v1)

Published 14 Feb 2024 in cs.CL

Abstract: Generating multiple translation candidates would enable users to choose the one that satisfies their needs. Although there has been work on diversified generation, there exists room for improving the diversity mainly because the previous methods do not address the overcorrection problem -- the model underestimates a prediction that is largely different from the training data, even if that prediction is likely. This paper proposes methods that generate more diverse translations by introducing perturbed k-nearest neighbor machine translation (kNN-MT). Our methods expand the search space of kNN-MT and help incorporate diverse words into candidates by addressing the overcorrection problem. Our experiments show that the proposed methods drastically improve candidate diversity and control the degree of diversity by tuning the perturbation's magnitude.

Citations (1)
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

We haven't generated follow-up questions for this paper yet.

X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube