Kavli Affiliate: Wei Gao
| First 5 Authors: Yongkang Liu, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang
| Summary:
Current end-to-end retrieval-based dialogue systems are mainly based on
Recurrent Neural Networks or Transformers with attention mechanisms. Although
promising results have been achieved, these models often suffer from slow
inference or huge number of parameters. In this paper, we propose a novel
lightweight fully convolutional architecture, called DialogConv, for response
selection. DialogConv is exclusively built on top of convolution to extract
matching features of context and response. Dialogues are modeled in 3D views,
where DialogConv performs convolution operations on embedding view, word view
and utterance view to capture richer semantic information from multiple
contextual views. On the four benchmark datasets, compared with
state-of-the-art baselines, DialogConv is on average about 8.5x smaller in
size, and 79.39x and 10.64x faster on CPU and GPU devices, respectively. At the
same time, DialogConv achieves the competitive effectiveness of response
selection.
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