Papers by Yixian Liu

2 papers
Towards Holistic and Automatic Evaluation of Open-Domain Dialogue Generation (2020.acl-main)

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Challenge: Existing methods of open-domain dialogue evaluation are labor-intensive and inefficient.
Approach: They propose to use open-domain dialogues to evaluate different aspects of dialogues using holistic evaluation metrics.
Outcome: The proposed metrics show strong correlations with human judgments.
Generalized Supervised Attention for Text Generation (2021.findings-acl)

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Challenge: Existing supervised attention methods that use human knowledge to learn better alignments are costly or infeasible.
Approach: They propose a generalized supervised attention method based on quasi alignments that are easier to obtain than ideal alignments.
Outcome: The proposed framework improves generation performance and is robust against errors in attention supervision.

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