AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets (2023.findings-acl)
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| Challenge: | Existing work on conversational recommendation systems lacks high-quality data . existing datasets lack large-scale and high-level data based on human annotators . |
| Approach: | They propose an automatic dataset synthesis approach that generates large-scale recommendation dialogues using structured graphs based on user-item information from the real world. |
| Outcome: | The proposed approach can generate large-scale and high-quality recommendation dialogues . it exploits user preferences, knowledge graphs, and conversation ability from existing datasets based on real-world data . |
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| Challenge: | Existing datasets for conversation summarization are small due to the lack of large-scale datasets. |
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| Challenge: | Existing generative methods to recommend items are shallowly integrated into the model training and have poor chit-chat ability. |
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| Challenge: | Current generation models fail to effectively utilize rich linguistic and world knowledge to generate coherent long text. |
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| Challenge: | Existing Conversational Recommender Systems (CRSs) deviate from real human interactions by rapidly recommending items in brief sessions. |
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| Challenge: | Conversational Recommendation System (CRS) is a rapidly growing research area, along with advancements in language modelling techniques. |
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| Challenge: | Existing Generative Commonsense Reasoning datasets are created using a small number of human annotators, covering only a narrow set of commonsense scenarios. |
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