Abstract
When re-finding items, users who forget or are uncertain about identifying details often rely on creative strategies for expressing their information needs—complex queries that describe content elements (e.g., book characters or events), information beyond the document text (e.g., descriptions of book covers), or personal context (e.g., when they read a book). Standard retrieval models that rely on lexical or semantic overlap between query and document text are challenged in such retrieval settings, known as tip-of-the-tongue (TOT) retrieval. We introduce a simple but effective framework for handling such complex queries by decomposing the query with an LLM into individual clues routing those as subqueries to specialized retrievers, and ensembling the results. Our approach takes advantage of off-the-shelf retrievers (e.g., CLIP for retrieving images of book covers) or incorporate retriever-specific logic (e.g., date constraints). We show that our framework incorporating query decomposition into retrievers can improve gold book recall up to 6% absolute gain for Recall@5 on a new collection of 14,441 real-world query-book pairs from an online community for resolving TOT inquiries.- Anthology ID:
- 2023.findings-emnlp.367
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5521–5533
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.367
- DOI:
- 10.18653/v1/2023.findings-emnlp.367
- Cite (ACL):
- Kevin Lin, Kyle Lo, Joseph Gonzalez, and Dan Klein. 2023. Decomposing Complex Queries for Tip-of-the-tongue Retrieval. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 5521–5533, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Decomposing Complex Queries for Tip-of-the-tongue Retrieval (Lin et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2023.findings-emnlp.367.pdf