LSTDial: Enhancing Dialogue Generation via Long- and Short-Term Measurement Feedback (2024.naacl-long)
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| Challenge: | Existing dialogue systems do not utilize quality dimensions specifically designed for dialogue evaluation to guide the response generation during training. |
| Approach: | They propose a two-stage framework which generates and utilizes conversation evaluation as explicit feedback during training. |
| Outcome: | The proposed framework generates and utilizes conversation evaluation as explicit feedback during training. |
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| Challenge: | Current approaches for controlling dialogue response generation focus on high-level attributes like style, sentiment, or topic. |
| Approach: | They propose a method that allows for more fine-grained control of dialogue response generation . they propose utterances that encourage the generation of control words in the future . |
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| Challenge: | Existing methods of open-domain dialogue evaluation are labor-intensive and inefficient. |
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Evaluating Dialogue Generation Systems via Response Selection (2020.acl-main)
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Generate, Evaluate, and Select: A Dialogue System with a Response Evaluator for Diversity-Aware Response Generation (2022.naacl-srw)
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| Challenge: | Existing dialogue systems that generate fluent responses are difficult to evaluate due to the one-to-many nature of dialogue, which means the existence of multiple appropriate responses is not appropriate. |
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| Challenge: | Existing evaluation metrics for response diversity do not capture the semantic diversity of generated responses. |
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Human-in-the-loop Abstractive Dialogue Summarization (2023.findings-acl)
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| Challenge: | Abstractive dialogue summarization systems are trained to maximize the likelihood of human-written summaries, but there is still a huge gap in generating high-quality summary as determined by humans. |
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