Multi-Domain Multilingual Question Answering

Sebastian Ruder, Avi Sil


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
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-tutorials.4.pdf
Code
 sebastianruder/emnlp2021-multiqa-tutorial
Data
DoQANatural QuestionsSQuAD