Search Results for author:
Found 15 papers, 7 papers with code
EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification
Knowledge-based authentication is crucial for task-oriented spoken dialogue systems that offer personalised and privacy-focused services.
Ranked #1 on
Speaker Identification
on EVI fr-FR
NLU++: A Multi-Label, Slot-Rich, Generalisable Dataset for Natural Language Understanding in Task-Oriented Dialogue
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.
ConvFiT: Conversational Fine-Tuning of Pretrained Language Models
Transformer-based language models (LMs) pretrained on large text collections are proven to store a wealth of semantic knowledge.
Efficient Intent Detection with Dual Sentence Encoders
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).
ConveRT: Efficient and Accurate Conversational Representations from Transformers
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 Reddit
PolyResponse: A Rank-based Approach to Task-Oriented Dialogue with Application in Restaurant Search and Booking
We present PolyResponse, a conversational search engine that supports task-oriented dialogue.
Training Neural Response Selection for Task-Oriented Dialogue Systems
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.
A Repository of Conversational Datasets
Progress in Machine Learning is often driven by the availability of large datasets, and consistent evaluation metrics for comparing modeling approaches.
BIG-bench Machine Learning
Conversational Response Selection
+1
Addressing Objects and Their Relations: The Conversational Entity Dialogue Model
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.
MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling
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.
Nearly Zero-Shot Learning for Semantic Decoding in Spoken Dialogue Systems
This paper presents two ways of dealing with scarce data in semantic decoding using N-Best speech recognition hypotheses.
Feudal Reinforcement Learning for Dialogue Management in Large Domains
Reinforcement learning (RL) is a promising approach to solve dialogue policy optimisation.
A Benchmarking Environment for Reinforcement Learning Based Task Oriented Dialogue Management
Dialogue assistants are rapidly becoming an indispensable daily aid.
Reward-Balancing for Statistical Spoken Dialogue Systems using Multi-objective Reinforcement Learning
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.
Multi-Objective Reinforcement Learning
reinforcement-learning
+2
Sub-domain Modelling for Dialogue Management with Hierarchical Reinforcement Learning
In doing that, we show that our approach has the potential to facilitate policy optimisation for more sophisticated multi-domain dialogue systems.
