| Revue: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560565 |
| ISSN: | 1405-5546 |
| Autores: | Butt, Sabur1 Ashraf, Noman1 Siddiqui, Muhammad Hammad Fahim2 Sidorov, Grigori1 Gelbukh, Alexander1 |
| Instituciones: | 1Instituto Politécnico Nacional, Centro de Investigación en Computación, México 2University of Ottawa, School of Electrical Engineering and Computer Science, Quebec. Canadá |
| Año: | 2021 |
| Periodo: | Ene-Mar |
| Volumen: | 25 |
| Número: | 1 |
| Paginación: | 23-32 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | The paper tackles the problem of question answering on social media data through an extractive approach. The task of question answering consists in obtaining an answer from the context given the context and a question. Our approach uses transformer models, which were fine-tuned on SQuAD. Usually, SQuAD is used for extractive question answering for comparing the results with human judgments in social media TweetQA dataset. Our experiments on multiple transformer models indicate the importance of application of pre-processing in the question answering on social media data and elucidates that extractive question answering fine-tuning even on other type of data can significantly improve the results reducing the gap with human evaluation. We use ROUGE, METEOR, and BLEU metrics. |
| Keyword: | Question answering, SQuAD, TweetQA, Social media, Tweets |
| Texte intégral: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
