A Multilingual Reading Comprehension System for more than 100 Languages
| Autor: | Radu Florian, Sara Rosenthal, Rishav Chakravarti, Avi Sil, Mihaela A. Bornea, Anthony Ferritto, Kazi Saidul Hasan, Salim Roukos |
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| Rok vydání: | 2020 |
| Předmět: |
Downstream (software development)
Machine translation Computer science business.industry 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Reading comprehension 0202 electrical engineering electronic engineering information engineering Question answering 020201 artificial intelligence & image processing Artificial intelligence business computer Natural language processing 0105 earth and related environmental sciences |
| Zdroj: | COLING (Demonstrations) |
| DOI: | 10.18653/v1/2020.coling-demos.8 |
| Popis: | This paper presents M-GAAMA, a Multilingual Question Answering architecture and demo system. This is the first multilingual machine reading comprehension (MRC) demo which is able to answer questions in over 100 languages. M-GAAMA answers questions from a given passage in the same or different language. It incorporates several existing multilingual models that can be used interchangeably in the demo such as M-BERT and XLM-R. The M-GAAMA demo also improves language accessibility by incorporating the IBM Watson machine translation widget to provide additional capabilities to the user to see an answer in their desired language. We also show how M-GAAMA can be used in downstream tasks by incorporating it into an END-TO-END-QA system using CFO (Chakravarti et al., 2019). We experiment with our system architecture on the Multi-Lingual Question Answering (MLQA) and the COVID-19 CORD (Wang et al., 2020; Tang et al., 2020) datasets to provide insights into the performance of the system. |
| Databáze: | OpenAIRE |
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