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Found 30 papers, 9 papers with code
$\textit{Dial BeInfo for Faithfulness}$: Improving Factuality of Information-Seeking Dialogue via Behavioural Fine-Tuning
Factuality is a crucial requirement in information seeking dialogue: the system should respond to the user's queries so that the responses are meaningful and aligned with the knowledge provided to the system.
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.
Multilingual and Cross-Lingual Intent Detection from Spoken Data
We present a systematic study on multilingual and cross-lingual intent detection from spoken data.
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
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. To address this fundamental obstacle, we introduce the Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully-labeled collection of human-human written conversations spanning over multiple domains and topics. At a size of 10k dialogues, it is at least one order of magnitude larger than all previous annotated task-oriented corpora. The contribution of this work apart from the open-sourced dataset is two-fold:firstly, a detailed description of the data collection procedure along with a summary of data structure and analysis is provided.
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.
Latent Topic Conversational Models
In this paper, we propose Latent Topic Conversational Model (LTCM) which augments seq2seq with a neural latent topic component to better guide response generation and make training easier.
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
+1
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.
Latent Intention Dialogue Models
Developing a dialogue agent that is capable of making autonomous decisions and communicating by natural language is one of the long-term goals of machine learning research.
Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding
This paper presents a deep learning architecture for the semantic decoder component of a Statistical Spoken Dialogue System.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Dialogue manager domain adaptation using Gaussian process reinforcement learning
Spoken dialogue systems allow humans to interact with machines using natural speech.
Neural Belief Tracker: Data-Driven Dialogue State Tracking
One of the core components of modern spoken dialogue systems is the belief tracker, which estimates the user's goal at every step of the dialogue.
Conditional Generation and Snapshot Learning in Neural Dialogue Systems
Recently a variety of LSTM-based conditional language models (LM) have been applied across a range of language generation tasks.
Continuously Learning Neural Dialogue Management
We describe a two-step approach for dialogue management in task-oriented spoken dialogue systems.
On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems
The ability to compute an accurate reward function is essential for optimising a dialogue policy via reinforcement learning.
A Network-based End-to-End Trainable Task-oriented Dialogue System
Teaching machines to accomplish tasks by conversing naturally with humans is challenging.
Multi-domain Neural Network Language Generation for Spoken Dialogue Systems
Moving from limited-domain natural language generation (NLG) to open domain is difficult because the number of semantic input combinations grows exponentially with the number of domains.
Counter-fitting Word Vectors to Linguistic Constraints
In this work, we present a novel counter-fitting method which injects antonymy and synonymy constraints into vector space representations in order to improve the vectors' capability for judging semantic similarity.
Reward Shaping with Recurrent Neural Networks for Speeding up On-Line Policy Learning in Spoken Dialogue Systems
Reward shaping is one promising technique for addressing these concerns.
Learning from Real Users: Rating Dialogue Success with Neural Networks for Reinforcement Learning in Spoken Dialogue Systems
The models are trained on dialogues generated by a simulated user and the best model is then used to train a policy on-line which is shown to perform at least as well as a baseline system using prior knowledge of the user's task.
Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems
Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact both on usability and perceived quality.
Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking
The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on.
Multi-domain Dialog State Tracking using Recurrent Neural Networks
Dialog state tracking is a key component of many modern dialog systems, most of which are designed with a single, well-defined domain in mind.
