89 citations · 92 across the 3 of their papers we have counts for
3 papers
Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks
Igor Shalyminov, Sungjin Lee, Arash Eshghi +1
Goal-oriented dialogue systems are now being widely adopted in industry where it is of key importance to maintain a rapid prototyping cycle for new products and domains. Data-drive…
Benchmarking Natural Language Understanding Services for building Conversational Agents
Xingkun Liu, Arash Eshghi, Pawel Swietojanski +1
We have recently seen the emergence of several publicly available Natural Language Understanding (NLU) toolkits, which map user utterances to structured, but more abstract, Dialogu…
Bootstrapping incremental dialogue systems: using linguistic knowledge to learn from minimal data
Dimitrios Kalatzis, Arash Eshghi, Oliver Lemon
We present a method for inducing new dialogue systems from very small amounts of unannotated dialogue data, showing how word-level exploration using Reinforcement Learning (RL), co…