Papers with feedback module

2 papers
Backtranslation Feedback Improves User Confidence in MT, Not Quality (2021.naacl-main)

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Challenge: Inbound translation is a modern need for which the user experience has significant room for improvement, beyond the basic machine translation facility.
Approach: They propose to provide cues that indicate the quality of MT output as well as suggest possible rephrasing of the source language.
Outcome: The proposed feedback module increases user confidence in the produced translation, but not the objective quality.
CASPER: Bridging Discrete and Continuous Prompt Optimization through Feedback-Guided Gradient Descent (2026.eacl-industry)

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Challenge: Existing pipelines for generative tasks require extensive manual effort and domain expertise to achieve task-optimal performance.
Approach: They propose a framework bridging discrete and continuous prompt optimization through feedback-guided gradient descent in embedding space.
Outcome: The proposed framework bridges discrete and continuous prompt optimization through feedback-guided gradient descent in embedding space.

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