Abstract
We present a novel response generation system. The system assumes the hypothesis that participants in a conversation base their response not only on previous dialog utterances but also on their background knowledge. Our model is based on a Recurrent Neural Network (RNN) that is trained over concatenated sequences of comments, a Convolution Neural Network that is trained over Wikipedia sentences and a formulation that couples the two trained embeddings in a multimodal space. We create a dataset of aligned Wikipedia sentences and sequences of Reddit utterances, which we we use to train our model. Given a sequence of past utterances and a set of sentences that represent the background knowledge, our end-to-end learnable model is able to generate context-sensitive and knowledge-driven responses by leveraging the alignment of two different data sources. Our approach achieves up to 55% improvement in perplexity compared to purely sequential models based on RNNs that are trained only on sequences of utterances.- Anthology ID:
- C16-1318
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 3370–3380
- Language:
- URL:
- https://aclanthology.org/C16-1318
- DOI:
- Cite (ACL):
- Pavlos Vougiouklis, Jonathon Hare, and Elena Simperl. 2016. A Neural Network Approach for Knowledge-Driven Response Generation. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 3370–3380, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- A Neural Network Approach for Knowledge-Driven Response Generation (Vougiouklis et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/C16-1318.pdf
- Code
- pvougiou/Aligning-Reddit-and-Wikipedia