Knowledge-based authentication is crucial for task-oriented spoken dialogue systems that offer personalised and privacy-focused services.
2) The ontology is divided into domain-specific and generic (i. e., domain-universal) intent modules that overlap across domains, promoting cross-domain reusability of annotated examples.
Transformer-based language models (LMs) pretrained on large text collections are proven to store a wealth of semantic knowledge.
Building conversational systems in new domains and with added functionality requires resource-efficient models that work under low-data regimes (i. e., in few-shot setups).
General-purpose pretrained sentence encoders such as BERT are not ideal for real-world conversational AI applications; they are computationally heavy, slow, and expensive to train.
Ranked #1 on Conversational Response Selection on PolyAI AmazonQA
We present PolyResponse, a conversational search engine that supports task-oriented dialogue.
Despite their popularity in the chatbot literature, retrieval-based models have had modest impact on task-oriented dialogue systems, with the main obstacle to their application being the low-data regime of most task-oriented dialogue tasks.
Progress in Machine Learning is often driven by the availability of large datasets, and consistent evaluation metrics for comparing modeling approaches.
Statistical spoken dialogue systems usually rely on a single- or multi-domain dialogue model that is restricted in its capabilities of modelling complex dialogue structures, e. g., relations.
Even though machine learning has become the major scene in dialogue research community, the real breakthrough has been blocked by the scale of data available.
This paper presents two ways of dealing with scarce data in semantic decoding using N-Best speech recognition hypotheses.
Reinforcement learning (RL) is a promising approach to solve dialogue policy optimisation.
Dialogue assistants are rapidly becoming an indispensable daily aid.
Reinforcement learning is widely used for dialogue policy optimization where the reward function often consists of more than one component, e. g., the dialogue success and the dialogue length.
In doing that, we show that our approach has the potential to facilitate policy optimisation for more sophisticated multi-domain dialogue systems.