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aMV-LSTM: an attention-based model with multiple positional text matching

Belkacem, Thiziri and Dkaki, Taoufiq and Moreno, José G. and Boughanem, Mohand aMV-LSTM: an attention-based model with multiple positional text matching. (2019) In: 34th ACM/SIGAPP Symposium on Applied Computing (SAC 2019), 8 April 2019 - 12 April 2019 (Limassol, Cyprus).

(Document in English)

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Official URL: https://doi.org/10.1145/3297280.3297355


Deep models are getting a wide interest in recent NLP and IR state-of-the-art. Among the proposed models, position-based models and attention-based models take into account the word position in the text, in the former, and the importance of a word among other words in the latter. The positional information are some of the important features that help text representation learning. However, the importance of a given word among others in a given text, which is an important aspect in text matching, is not considered in positional features. In this paper, we propose a model that combines position-based representation learning approach with the attention-based weighting process. The latter learns an importance coefficient for each word of the input text. We propose an extension of a position-based model MV-LSTM with an attention layer, allowing a parameterizable architecture. We believe that when the model is aware of both word position and importance, the learned representations will get more relevant features for the matching process. Our model, namely aMV-LSTM, learns the attention based coefficients to weight words of the different input sentences, before computing their position-based representations. Experimental results, in question/answer matching and question pairs identification tasks, show that the proposed model outperforms the MV-LSTM baseline and several state-of-the-art models.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to ACM. The definitive version is available at http://dl.acm.org This papers appears in SAC'19 ISBN: 978-1-4503-5933-7 The original PDF is available at: https://dl.acm.org/citation.cfm?id=3297355
HAL Id:hal-02441990
Audience (conference):National conference proceedings
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Laboratory name:
Deposited On:11 Dec 2019 15:51

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