Attention-based Conditioning Methods for External Knowledge Integration

Katerina Margatina, Christos Baziotis, Alexandros Potamianos


Abstract
In this paper, we present a novel approach for incorporating external knowledge in Recurrent Neural Networks (RNNs). We propose the integration of lexicon features into the self-attention mechanism of RNN-based architectures. This form of conditioning on the attention distribution, enforces the contribution of the most salient words for the task at hand. We introduce three methods, namely attentional concatenation, feature-based gating and affine transformation. Experiments on six benchmark datasets show the effectiveness of our methods. Attentional feature-based gating yields consistent performance improvement across tasks. Our approach is implemented as a simple add-on module for RNN-based models with minimal computational overhead and can be adapted to any deep neural architecture.
Anthology ID:
P19-1385
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3944–3951
Language:
URL:
https://aclanthology.org/P19-1385
DOI:
10.18653/v1/P19-1385
Bibkey:
Cite (ACL):
Katerina Margatina, Christos Baziotis, and Alexandros Potamianos. 2019. Attention-based Conditioning Methods for External Knowledge Integration. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3944–3951, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Attention-based Conditioning Methods for External Knowledge Integration (Margatina et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1385.pdf
Code
 mourga/affective-attention
Data
SSTSST-5