Predicting Helpful Posts in Open-Ended Discussion Forums: A Neural Architecture

Kishaloy Halder, Min-Yen Kan, Kazunari Sugiyama


Abstract
Users participate in online discussion forums to learn from others and share their knowledge with the community. They often start a thread with a question or by sharing their new findings on a certain topic. We find that, unlike Community Question Answering, where questions are mostly factoid based, the threads in a forum are often open-ended (e.g., asking for recommendations from others) without a single correct answer. In this paper, we address the task of identifying helpful posts in a forum thread to help users comprehend long running discussion threads, which often contain repetitive or irrelevant posts. We propose a recurrent neural network based architecture to model (i) the relevance of a post regarding the original post starting the thread and (ii) the novelty it brings to the discussion, compared to the previous posts in the thread. Experimental results on different types of online forum datasets show that our model significantly outperforms the state-of-the-art neural network models for text classification.
Anthology ID:
N19-1318
Original:
N19-1318v1
Version 2:
N19-1318v2
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3148–3157
Language:
URL:
https://aclanthology.org/N19-1318
DOI:
10.18653/v1/N19-1318
Bibkey:
Cite (ACL):
Kishaloy Halder, Min-Yen Kan, and Kazunari Sugiyama. 2019. Predicting Helpful Posts in Open-Ended Discussion Forums: A Neural Architecture. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3148–3157, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Predicting Helpful Posts in Open-Ended Discussion Forums: A Neural Architecture (Halder et al., NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1318.pdf
Video:
 https://vimeo.com/347421182