89 citations · 108 across the 6 of their papers we have counts for
11 papers
A Study of Automatic Metrics for the Evaluation of Natural Language Explanations
Miruna Clinciu, Arash Eshghi, Helen Hastie
As transparency becomes key for robotics and AI, it will be necessary to evaluate the methods through which transparency is provided, including automatically generated natural lang…
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…
Current Challenges in Spoken Dialogue Systems and Why They Are Critical for Those Living with Dementia
Angus Addlesee, Arash Eshghi, Ioannis Konstas
Dialogue technologies such as Amazon's Alexa have the potential to transform the healthcare industry. However, current systems are not yet naturally interactive: they are often tur…
Few-Shot Dialogue Generation Without Annotated Data: A Transfer Learning Approach
Igor Shalyminov, Sungjin Lee, Arash Eshghi +1
Learning with minimal data is one of the key challenges in the development of practical, production-ready goal-oriented dialogue systems. In a real-world enterprise setting where d…
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…
Multi-Task Learning for Domain-General Spoken Disfluency Detection in Dialogue Systems
Igor Shalyminov, Arash Eshghi, Oliver Lemon
Spontaneous spoken dialogue is often disfluent, containing pauses, hesitations, self-corrections and false starts. Processing such phenomena is essential in understanding a speaker…