7 citations · 31 across the 10 of their papers we have counts for
11 papers
Data-Efficient Methods for Dialogue Systems
Igor Shalyminov
Conversational User Interface (CUI) has become ubiquitous in everyday life, in consumer-focused products like Siri and Alexa or business-oriented solutions. Deep learning underlies…
Hybrid Generative-Retrieval Transformers for Dialogue Domain Adaptation
Igor Shalyminov, Alessandro Sordoni, Adam Atkinson +1
Domain adaptation has recently become a key problem in dialogue systems research. Deep learning, while being the preferred technique for modeling such systems, works best given mas…
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…
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…
Contextual Out-of-Domain Utterance Handling With Counterfeit Data Augmentation
Sungjin Lee, Igor Shalyminov
Neural dialog models often lack robustness to anomalous user input and produce inappropriate responses which leads to frustrating user experience. Although there are a set of prior…
Improving Robustness of Neural Dialog Systems in a Data-Efficient Way with Turn Dropout
Igor Shalyminov, Sungjin Lee
Neural network-based dialog models often lack robustness to anomalous, out-of-domain (OOD) user input which leads to unexpected dialog behavior and thus considerably limits such mo…
Neural Response Ranking for Social Conversation: A Data-Efficient Approach
Igor Shalyminov, Ondřej Dušek, Oliver Lemon
The overall objective of 'social' dialogue systems is to support engaging, entertaining, and lengthy conversations on a wide variety of topics, including social chit-chat. Apart fr…
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…
An Ensemble Model with Ranking for Social Dialogue
Ioannis Papaioannou, Amanda Cercas Curry, Jose L. Part +6
Open-domain social dialogue is one of the long-standing goals of Artificial Intelligence. This year, the Amazon Alexa Prize challenge was announced for the first time, where real c…
Bootstrapping incremental dialogue systems from minimal data: the generalisation power of dialogue grammars
Arash Eshghi, Igor Shalyminov, Oliver Lemon
We investigate an end-to-end method for automatically inducing task-based dialogue systems from small amounts of unannotated dialogue data. It combines an incremental semantic gram…
Challenging Neural Dialogue Models with Natural Data: Memory Networks Fail on Incremental Phenomena
Igor Shalyminov, Arash Eshghi, Oliver Lemon
Natural, spontaneous dialogue proceeds incrementally on a word-by-word basis; and it contains many sorts of disfluency such as mid-utterance/sentence hesitations, interruptions, an…