Community Question Answering (CQA) websites have become a very popular and useful source of information, which helps users to find out answers to their corresponding questions. On one hand, if a user's question does not exist in the forum, a new post is created so that other users can contribute and provide answers or comments. On the other hand, if similar or related questions already exist in the forum, the system should be able to detect them and redirect the user towards the corresponding threads. This procedure of detecting similar questions is also known as question-to-question similarity task in the NLP research community. Once the correct posts have been detected, it is important to provide the correct answer since some posts can contain tens or hundreds of answers/comments which make the user's research more difficult. This procedure is also known as the question-answering similarity task. In this paper, we address both tasks and aim at providing the first framework on the evaluation of similar questions and question-answering detection on a multi-domain corpora. For that purpose, we use the community question answering forum Stack-Exchange to extract posts and pairs of questions and answers from multiple domains. We evaluate two baseline approaches over 19 domains and provide preliminary results on multiple annotated question-answering datasets to deal with question-answering similarity task.
@InProceedings{HAZEM18.34, author = {Amir Hazem and Basma El Amel Boussaha and Nicolas Hernandez}, title = "{A Multi-Domain Framework for Textual Similarity. A Case Study on Question-to-Question and Question-Answering Similarity Tasks}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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