Abstract
We propose Chain-of-Questions, a framework that trains a model to robustly answer multistep questions by generating and answering sub-questions. We obtain supervision for sub-questions from human-annotated question decomposition meaning representation (QDMR), but QDMR does not include annotated answers to sub-questions. To overcome this technical challenge, we treat sub-answers as latent variables and infer them with a novel dynamic mixture of Hard-EM and MAPO. Chain-of-Questions is effective and robust, greatly outperforming strong neuro-symbolic methods by 9.0 F1 on a DROP contrast set and GPT-3.5 by 24.3 F1 on a HotpotQA adversarial set.- Anthology ID:
- 2023.emnlp-main.547
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8845–8860
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.547
- DOI:
- 10.18653/v1/2023.emnlp-main.547
- Cite (ACL):
- Wang Zhu, Jesse Thomason, and Robin Jia. 2023. Chain-of-Questions Training with Latent Answers for Robust Multistep Question Answering. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 8845–8860, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Chain-of-Questions Training with Latent Answers for Robust Multistep Question Answering (Zhu et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2023.emnlp-main.547.pdf