A Context-aware Convolutional Natural Language Generation model for Dialogue Systems

Sourab Mangrulkar, Suhani Shrivastava, Veena Thenkanidiyoor, Dileep Aroor Dinesh


Abstract
Natural language generation (NLG) is an important component in spoken dialog systems (SDSs). A model for NLG involves sequence to sequence learning. State-of-the-art NLG models are built using recurrent neural network (RNN) based sequence to sequence models (Ondřej Dušek and Filip Jurčíček, 2016a). Convolutional sequence to sequence based models have been used in the domain of machine translation but their application as Natural Language Generators in dialogue systems is still unexplored. In this work, we propose a novel approach to NLG using convolutional neural network (CNN) based sequence to sequence learning. CNN-based approach allows to build a hierarchical model which encapsulates dependencies between words via shorter path unlike RNNs. In contrast to recurrent models, convolutional approach allows for efficient utilization of computational resources by parallelizing computations over all elements, and eases the learning process by applying constant number of nonlinearities. We also propose to use CNN-based reranker for obtaining responses having semantic correspondence with input dialogue acts. The proposed model is capable of entrainment. Studies using a standard dataset shows the effectiveness of the proposed CNN-based approach to NLG.
Anthology ID:
W18-5020
Volume:
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Kazunori Komatani, Diane Litman, Kai Yu, Alex Papangelis, Lawrence Cavedon, Mikio Nakano
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
191–200
Language:
URL:
https://aclanthology.org/W18-5020
DOI:
10.18653/v1/W18-5020
Bibkey:
Cite (ACL):
Sourab Mangrulkar, Suhani Shrivastava, Veena Thenkanidiyoor, and Dileep Aroor Dinesh. 2018. A Context-aware Convolutional Natural Language Generation model for Dialogue Systems. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, pages 191–200, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
A Context-aware Convolutional Natural Language Generation model for Dialogue Systems (Mangrulkar et al., SIGDIAL 2018)
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PDF:
https://aclanthology.org/W18-5020.pdf