Eugene Yang
Dawn Lawrie
ABSTRACT
This half day tutorial introduces the participant to the basic concepts underlying neural Cross-Language Information Retrieval (CLIR). It discusses the most common algorithmic approaches to CLIR, focusing on modern neural methods; the history of CLIR; where to find and how to use CLIR training collections, test collections and baseline systems; how CLIR training and test collections are constructed; and open research questions in CLIR.
- Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, et al. 2016. MS MARCO: A human generated machine reading comprehension dataset. arXiv preprint arXiv:1611.09268 (2016).Google Scholar
- Petra Galuscáková, Douglas W Oard, and Suraj Nair. 2021. Cross-language information retrieval. arXiv preprint arXiv:2111.05988 (2021).Google Scholar
- Sean MacAvaney, Andrew Yates, Sergey Feldman, Doug Downey, Arman Cohan, and Nazli Goharian. 2021. Simplified Data Wrangling with ir_datasets. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR).Google ScholarDigital Library
- Jian-Yun Nie. 2010. Cross-Language Information Retrieval. Morgan & Claypool Publishers. https://doi.org/10.2200/S00266ED1V01Y201005HLT008Google ScholarCross Ref
Index Terms
- Neural Methods for Cross-Language Information Retrieval
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