Abstract
Community Question Answering (CQA) is an Information Retrieval (IR) task that allows matching complex subjective questions and candidate answers based on user posts in community web forums. User questions and comment-based answers deal with many problems, such as redundancy or ambiguity of linguistic information. In this paper, we propose a pairwise learning-to-rank model community QA model in the home improvement domain. For a user question, this model must rank candidate answers in order of relevance. Our main contribution consists of transformer-based language models using user tags to accurate the model generalisation. To train our model, we also propose a proper CQA dataset in home improvement domain that consists of information extracted from community forums. We evaluate our approach by comparing the performance based on analysis with the state-of-the-art method on text or document similarity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chung, J., Gülçehre, Ç., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. CoRR abs/1412.3555 (2014)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, Minnesota, vol. 1, pp. 4171–4186 (2019)
He, H., Gimpel, K., Lin, J.: Multi-perspective sentence similarity modeling with convolutional neural networks. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1576–1586. Association for Computational Linguistics (ACL), Lisbon, September 2015
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015, Conference Track Proceedings, San Diego, CA, USA, 7–9 May 2015 (2015)
Loshchilov, I., Hutter, F.: Fixing weight decay regularization. In: Proceedings of Seventh International Conference on Learning Representations (ICLR 2019) (2019)
Maia, M., Sales, J.E., Freitas, A., Handschuh, S., Endres, M.: A comparative study of deep neural network models on multi-label text classification in finance. In: 15th IEEE International Conference on Semantic Computing, ICSC 2021, Laguna Hills, CA, USA, 27–29 January 2021. IEEE Computer Society (2021)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, NIPS 2013, USA, vol. 2, pp. 3111–3119 (2013)
Nakov, P., et al.: SemEval-2017 task 3: community question answering. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 27–48. Association for Computational Linguistics, Vancouver, August 2017
Nakov, P., et al.: SemEval-2016 task 3: community question answering. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pp. 525–545. Association for Computational Linguistics, San Diego, June 2016
Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543. Association for Computational Linguistics, Doha, October 2014
Peters, M., et al.: Deep contextualized word representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 2227–2237. Association for Computational Linguistics, New Orleans, June 2018
Sanh, V., Debut, L., Chaumond, J., Wolf, T.: DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. In: NeurIPS EMC\(^{2}\) Workshop (2019)
Vaswani, A., et al.: Attention is all you need. CoRR (2017)
Wang, Z., Hamza, W., Florian, R.: Bilateral multi-perspective matching for natural language sentences. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, IJCAI 2017, pp. 4144–4150. AAAI Press (2017)
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., Le, Q.V.: XLNet: generalized autoregressive pretraining for language understanding. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32. pp. 5753–5763 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Maia, M., Handschuh, S., Endres, M. (2021). A Tag-Based Transformer Community Question Answering Learning-to-Rank Model in the Home Improvement Domain. In: Strauss, C., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2021. Lecture Notes in Computer Science(), vol 12924. Springer, Cham. https://doi.org/10.1007/978-3-030-86475-0_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-86475-0_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86474-3
Online ISBN: 978-3-030-86475-0
eBook Packages: Computer ScienceComputer Science (R0)