Estimating the Usefulness of Clarifying Questions and Answers for Conversational Search
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917572075585536 |
|---|---|
| author | Sekulić, Ivan Łajewska, Weronika Balog, Krisztian Crestani, Fabio |
| author_facet | Sekulić, Ivan Łajewska, Weronika Balog, Krisztian Crestani, Fabio |
| contents | While the body of research directed towards constructing and generating clarifying questions in mixed-initiative conversational search systems is vast, research aimed at processing and comprehending users' answers to such questions is scarce. To this end, we present a simple yet effective method for processing answers to clarifying questions, moving away from previous work that simply appends answers to the original query and thus potentially degrades retrieval performance. Specifically, we propose a classifier for assessing usefulness of the prompted clarifying question and an answer given by the user. Useful questions or answers are further appended to the conversation history and passed to a transformer-based query rewriting module. Results demonstrate significant improvements over strong non-mixed-initiative baselines. Furthermore, the proposed approach mitigates the performance drops when non useful questions and answers are utilized. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_11463 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Estimating the Usefulness of Clarifying Questions and Answers for Conversational Search Sekulić, Ivan Łajewska, Weronika Balog, Krisztian Crestani, Fabio Information Retrieval Computation and Language While the body of research directed towards constructing and generating clarifying questions in mixed-initiative conversational search systems is vast, research aimed at processing and comprehending users' answers to such questions is scarce. To this end, we present a simple yet effective method for processing answers to clarifying questions, moving away from previous work that simply appends answers to the original query and thus potentially degrades retrieval performance. Specifically, we propose a classifier for assessing usefulness of the prompted clarifying question and an answer given by the user. Useful questions or answers are further appended to the conversation history and passed to a transformer-based query rewriting module. Results demonstrate significant improvements over strong non-mixed-initiative baselines. Furthermore, the proposed approach mitigates the performance drops when non useful questions and answers are utilized. |
| title | Estimating the Usefulness of Clarifying Questions and Answers for Conversational Search |
| topic | Information Retrieval Computation and Language |
| url | https://arxiv.org/abs/2401.11463 |