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Found 11 papers, 4 papers with code
Data-Efficient Methods for Dialogue Systems
In this thesis, we address the above issues by introducing a series of methods for training robust dialogue systems from minimal data.
Hybrid Generative-Retrieval Transformers for Dialogue Domain Adaptation
Domain adaptation has recently become a key problem in dialogue systems research.
Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks
Our main dataset is the Stanford Multi-Domain dialogue corpus.
Few-Shot Dialogue Generation Without Annotated Data: A Transfer Learning Approach
Learning with minimal data is one of the key challenges in the development of practical, production-ready goal-oriented dialogue systems.
Contextual Out-of-Domain Utterance Handling With Counterfeit Data Augmentation
Neural dialog models often lack robustness to anomalous user input and produce inappropriate responses which leads to frustrating user experience.
Improving Robustness of Neural Dialog Systems in a Data-Efficient Way with Turn Dropout
We present a new dataset for studying the robustness of dialog systems to OOD input, which is bAbI Dialog Task 6 augmented with OOD content in a controlled way.
Neural Response Ranking for Social Conversation: A Data-Efficient Approach
Using a dataset of real conversations collected in the 2017 Alexa Prize challenge, we developed a neural ranker for selecting 'good' system responses to user utterances, i. e. responses which are likely to lead to long and engaging conversations.
Multi-Task Learning for Domain-General Spoken Disfluency Detection in Dialogue Systems
To test the model's generalisation potential, we evaluate the same model on the bAbI+ dataset, without any additional training.
An Ensemble Model with Ranking for Social Dialogue
Open-domain social dialogue is one of the long-standing goals of Artificial Intelligence.
Challenging Neural Dialogue Models with Natural Data: Memory Networks Fail on Incremental Phenomena
Results show that the semantic accuracy of the MemN2N model drops drastically; and that although it is in principle able to learn to process the constructions in bAbI+, it needs an impractical amount of training data to do so.
Bootstrapping incremental dialogue systems from minimal data: the generalisation power of dialogue grammars
Our experiments show that our model can process 74% of the Facebook AI bAbI dataset even when trained on only 0. 13% of the data (5 dialogues).
