Summary of Efficient and Interpretable Information Retrieval For Product Question Answering with Heterogeneous Data, by Biplob Biswas et al.
Efficient and Interpretable Information Retrieval for Product Question Answering with Heterogeneous Data
by Biplob Biswas, Rajiv Ramnath
First submitted to arxiv on: 21 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The proposed hybrid information retrieval mechanism learns a dense semantic representation and a sparse lexical representation, which are then combined for ranking candidate information. The architecture consists of dual encoders that learn to encode queries and information elements separately. Each encoder is trained using contrastive learning, which enhances the expansion-enhanced sparse lexical representation. This approach improves information retrieval by minimizing vocabulary mismatch problems during lexical matching. In a benchmark product question-answering dataset, the hybrid model outperforms independently trained retrievers in terms of MRR@5 score, while also offering better interpretability and reducing response time and computational load. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to search for information on the internet is presented. This method combines two ways of understanding text: a dense semantic representation that sees the meaning behind words, and a sparse lexical representation that looks at individual words. The system uses these representations together to rank the most relevant answers to a question. In tests using real-world data, this approach outperforms other methods by 10-20%. It also provides more insight into why certain answers are ranked highly, and does so quickly and efficiently. |
Keywords
» Artificial intelligence » Encoder » Question answering
