Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentation (2023.findings-emnlp)
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| Challenge: | Conversational Recommendation System (CRS) is a rapidly growing research area, along with advancements in language modelling techniques. |
| Approach: | They propose to use a benchmark dataset to develop CRS models and address biases arising from feedback loop inherent in multi-turn interactions to enhance model performance while mitigating biase. |
| Outcome: | The proposed strategies improve on ReDial and TG-ReDial benchmark datasets and offer additional insights on addressing multiple newly formulated biases. |
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