└── README.md


/README.md:
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 1 | # EMNLP 2021 Tutorial: Multi-Domain Multilingual Question Answering
 2 | 
 3 | You can find the link to the conference session on Underline [here](https://underline.io/events/192/sessions?eventSessionId=7844) if you have registered for the conference.
 4 | 
 5 | The slides are publicly available at: https://tinyurl.com/multi-qa-tutorial
 6 | 
 7 | If you found the content of [the tutorial](https://aclanthology.org/2021.emnlp-tutorials.4/) helpful, consider citing it as:
 8 | 
 9 | ```
10 | @inproceedings{ruder-sil-2021-multi,
11 |     title = "Multi-Domain Multilingual Question Answering",
12 |     author = "Ruder, Sebastian  and
13 |       Sil, Avi",
14 |     booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
15 |     month = nov,
16 |     year = "2021",
17 |     address = "Punta Cana, Dominican Republic {\&} Online",
18 |     publisher = "Association for Computational Linguistics",
19 |     url = "https://aclanthology.org/2021.emnlp-tutorials.4",
20 |     pages = "17--21",
21 |     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.",
22 | }
23 | ```
24 | 


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