Summary of Mitigating the Negative Impact Of Over-association For Conversational Query Production, by Ante Wang et al.
Mitigating the Negative Impact of Over-association for Conversational Query Production
by Ante Wang, Linfeng Song, Zijun Min, Ge Xu, Xiaoli Wang, Junfeng Yao, Jinsong Su
First submitted to arxiv on: 29 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle the challenge of conversational query generation for knowledge-based dialogue systems. The goal is to produce search queries from dialogue histories that can retrieve relevant information from a search engine. Previous models have struggled with the “data hunger” issue, where they either drop important concepts or generate irrelevant ones. This problem is attributed to the “over-association” phenomenon, where many gold queries are indirectly related to the conversation topics due to annotators’ background knowledge. The authors analyze this issue and propose instance-level weighting strategies to mitigate its effects from multiple perspectives. Experimental results on two benchmarks (Wizard-of-Internet and DuSinc) show that these strategies lead to significant performance gains (2%-5% across automatic metrics and human evaluation). Moreover, the model selects better concepts from dialogue histories and is 10 times more data efficient than the baseline. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Conversational query generation is a technique used in knowledge-based dialogue systems. These systems try to help users find information by generating search queries based on what they say. But this can be tricky because previous models have had trouble making sure they include all the important details and don’t add unnecessary ones. The problem is that some of the “right” answers are related to each other in a way that’s hard to understand, so it makes it harder for the model to learn what’s really important. To solve this, researchers came up with new ways to train their models, which make them better at picking out the right information and using less data. |
