Search Results for author: Pei-Hao Su

Found 29 papers, 6 papers with code

Training Neural Response Selection for Task-Oriented Dialogue Systems

1 code implementation ACL 2019 Matthew Henderson, Ivan Vulić, Daniela Gerz, Iñigo Casanueva, Paweł Budzianowski, Sam Coope, Georgios Spithourakis, Tsung-Hsien Wen, Nikola Mrkšić, Pei-Hao Su

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.

Chatbot Language Modelling +1

Deep Learning for Conversational AI

no code implementations NAACL 2018 Pei-Hao Su, Nikola Mrk{\v{s}}i{\'c}, I{\~n}igo Casanueva, Ivan Vuli{\'c}

The main purpose of this tutorial is to encourage dialogue research in the NLP community by providing the research background, a survey of available resources, and giving key insights to application of state-of-the-art SDS methodology into industry-scale conversational AI systems.

Decision Making Dialogue Management +4

Sample Efficient Deep Reinforcement Learning for Dialogue Systems with Large Action Spaces

no code implementations11 Feb 2018 Gellért Weisz, Paweł Budzianowski, Pei-Hao Su, Milica Gašić

A part of this effort is the policy optimisation task, which attempts to find a policy describing how to respond to humans, in the form of a function taking the current state of the dialogue and returning the response of the system.

reinforcement-learning Spoken Dialogue Systems

Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management

no code implementations WS 2017 Pei-Hao Su, Pawel Budzianowski, Stefan Ultes, Milica Gasic, Steve Young

Firstly, to speed up the learning process, two sample-efficient neural networks algorithms: trust region actor-critic with experience replay (TRACER) and episodic natural actor-critic with experience replay (eNACER) are presented.

Dialogue Management reinforcement-learning

Multi-domain Neural Network Language Generation for Spoken Dialogue Systems

no code implementations NAACL 2016 Tsung-Hsien Wen, Milica Gasic, Nikola Mrksic, Lina M. Rojas-Barahona, Pei-Hao Su, David Vandyke, Steve Young

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.

Domain Adaptation Spoken Dialogue Systems +1

Counter-fitting Word Vectors to Linguistic Constraints

2 code implementations NAACL 2016 Nikola Mrkšić, Diarmuid Ó Séaghdha, Blaise Thomson, Milica Gašić, Lina Rojas-Barahona, Pei-Hao Su, David Vandyke, Tsung-Hsien Wen, Steve Young

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.

Dialogue State Tracking Semantic Similarity +1

Learning from Real Users: Rating Dialogue Success with Neural Networks for Reinforcement Learning in Spoken Dialogue Systems

no code implementations13 Aug 2015 Pei-Hao Su, David Vandyke, Milica Gasic, Dongho Kim, Nikola Mrksic, Tsung-Hsien Wen, Steve Young

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.

Spoken Dialogue Systems

Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking

no code implementations WS 2015 Tsung-Hsien Wen, Milica Gasic, Dongho Kim, Nikola Mrksic, Pei-Hao Su, David Vandyke, Steve Young

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.

Text Generation

Multi-domain Dialog State Tracking using Recurrent Neural Networks

no code implementations IJCNLP 2015 Nikola Mrkšić, Diarmuid Ó Séaghdha, Blaise Thomson, Milica Gašić, Pei-Hao Su, David Vandyke, Tsung-Hsien Wen, Steve Young

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.

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