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Towards coherent and engaging spoken dialog response generation us- ing automatic conversation evaluators

Sanghyun Yi, Rahul Goel, Chandra Khatri, Tagyoung Chung, Behnam Hedayatnia, Anu Venkatesh, Raefer Gabriel, Dilek Hakkani-Tur · 2019 · arXiv 1904.13015

3 Pith papers cite this work. Polarity classification is still indexing.

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cs.CL 3

years

2020 1 2019 2

verdicts

UNVERDICTED 2 CONDITIONAL 1

representative citing papers

Learning to summarize from human feedback

cs.CL · 2020-09-02 · conditional · novelty 7.0

Reinforcement learning on a reward model trained from human summary comparisons produces summaries humans prefer over supervised fine-tuning or human references on TL;DR and transfers to CNN/DM.

Fine-Tuning Language Models from Human Preferences

cs.CL · 2019-09-18 · unverdicted · novelty 7.0

Language models fine-tuned via RL on 5k-60k human preference comparisons produce stylistically better text continuations and human-preferred summaries that sometimes copy input sentences.

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Showing 3 of 3 citing papers.

  • Learning to summarize from human feedback cs.CL · 2020-09-02 · conditional · none · ref 68

    Reinforcement learning on a reward model trained from human summary comparisons produces summaries humans prefer over supervised fine-tuning or human references on TL;DR and transfers to CNN/DM.

  • Fine-Tuning Language Models from Human Preferences cs.CL · 2019-09-18 · unverdicted · none · ref 29

    Language models fine-tuned via RL on 5k-60k human preference comparisons produce stylistically better text continuations and human-preferred summaries that sometimes copy input sentences.

  • A Modular Task-oriented Dialogue System Using a Neural Mixture-of-Experts cs.CL · 2019-07-10 · unverdicted · none · ref 34

    MTDS with TokenMoE improves inform rate by 8.1% and success rate by 0.8% over single-module baselines on a benchmark dataset.