Transformer-Based Extractive Social Media Question Answering on TweetQA

Authors

  • Sabur Butt Instituto Politécnico Nacional, Centro de Investigación en Computación
  • Noman Ashraf Instituto Politécnico Nacional, Centro de Investigación en Computación
  • Muhammad Hammad Fahim Siddiqui Instituto Politécnico Nacional, Centro de Investigación en Computación
  • Grigori Sidorov Instituto Politécnico Nacional, Centro de Investigación en Computación
  • Alexander Gelbukh Instituto Politécnico Nacional, Centro de Investigación en Computación

DOI:

https://doi.org/10.13053/cys-25-1-3897

Keywords:

Question answering, SQuAD, tweetQA, social media, tweets

Abstract

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 mediaTweetQA 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.

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Published

2021-02-15

Issue

Vol. 25 No. 1 (2021): 25(1) 2021

Section

Articles

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