Abstract
Question answering (QA) is one of the most challenging and impactful tasks in natural language processing. Most research in QA, however, has focused on the open-domain or monolingual setting while most real-world applications deal with specific domains or languages. In this tutorial, we attempt to bridge this gap. Firstly, we introduce standard benchmarks in multi-domain and multilingual QA. In both scenarios, we discuss state-of-the-art approaches that achieve impressive performance, ranging from zero-shot transfer learning to out-of-the-box training with open-domain QA systems. Finally, we will present open research problems that this new research agenda poses such as multi-task learning, cross-lingual transfer learning, domain adaptation and training large scale pre-trained multilingual language models.- Anthology ID:
- 2021.emnlp-tutorials.4
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic & Online
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 17–21
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-tutorials.4
- DOI:
- 10.18653/v1/2021.emnlp-tutorials.4
- Cite (ACL):
- Sebastian Ruder and Avi Sil. 2021. Multi-Domain Multilingual Question Answering. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts, pages 17–21, Punta Cana, Dominican Republic & Online. Association for Computational Linguistics.
- Cite (Informal):
- Multi-Domain Multilingual Question Answering (Ruder & Sil, EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2021.emnlp-tutorials.4.pdf
- Code
- sebastianruder/emnlp2021-multiqa-tutorial
- Data
- DoQA, Natural Questions, SQuAD