Improving the Community Question Retrieval Performance Using Attention-based Siamese LSTM

Nouha Othman (1, 2) , Rim Faiz (2) , Kamel Smaïli (1)

1 SMarT - Statistical Machine Translation and Speech Modelization and Text
2 LARODEC - Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus

In this paper, we focus on the problem of question retrieval in community Question Answering (cQA) which aims to retrieve from the community archives the previous questions that are semantically equivalent to the new queries. The major challenges in this crucial task are the shortness of the questions as well as the word mismatch problem as users can formulate the same query using different wording. While numerous attempts have been made to address this problem, most existing methods relied on supervised models which significantly depend on large training data sets and manual feature engineering. Such methods are mostly constrained by their specificities that put aside the word order and ignore syntactic and semantic relationships. In this work, we rely on Neural Networks (NNs) which can learn rich dense representations of text data and enable the prediction of the textual similarity between the community questions. We propose a deep learning approach based on a Siamese architecture with LSTM networks, augmented with an attention mechanism. We test different similarity measures to predict the semantic similarity between the community questions. Experiments conducted on real cQA data sets in English and Arabic show that the performance of question retrieval is improved as compared to other competitive methods.

Mots clés: Community question answering; Question retrieval; Siamese LSTM; Attention mechanism

Domaines: Informatique et langage [cs.CL]

Nouha Othman, Rim Faiz, Kamel Smaïli. Improving the Community Question Retrieval Performance Using Attention-based Siamese LSTM. NLDB2020: 25th International Conference on Natural Language & Information Systems, Jun 2020, Saarbrücken, Germany. ⟨hal-02867309⟩