AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue

Gaurav Kumar, Rishabh Joshi, Jaspreet Singh, Promod Yenigalla


Abstract
The problem of building a coherent and non-monotonous conversational agent with proper discourse and coverage is still an area of open research. Current architectures only take care of semantic and contextual information for a given query and fail to completely account for syntactic and external knowledge which are crucial for generating responses in a chit-chat system. To overcome this problem, we propose an end to end multi-stream deep learning architecture that learns unified embeddings for query-response pairs by leveraging contextual information from memory networks and syntactic information by incorporating Graph Convolution Networks (GCN) over their dependency parse. A stream of this network also utilizes transfer learning by pre-training a bidirectional transformer to extract semantic representation for each input sentence and incorporates external knowledge through the neighborhood of the entities from a Knowledge Base (KB). We benchmark these embeddings on the next sentence prediction task and significantly improve upon the existing techniques. Furthermore, we use AMUSED to represent query and responses along with its context to develop a retrieval based conversational agent which has been validated by expert linguists to have comprehensive engagement with humans.
Anthology ID:
2020.lrec-1.94
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
750–758
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.94
DOI:
Bibkey:
Cite (ACL):
Gaurav Kumar, Rishabh Joshi, Jaspreet Singh, and Promod Yenigalla. 2020. AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 750–758, Marseille, France. European Language Resources Association.
Cite (Informal):
AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue (Kumar et al., LREC 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.lrec-1.94.pdf
Data
GLUE