Search Results for author: Paweł Budzianowski

Found 19 papers, 6 papers with code

NLU++: A Multi-Label, Slot-Rich, Generalisable Dataset for Natural Language Understanding in Task-Oriented Dialogue

1 code implementation27 Apr 2022 Iñigo Casanueva, Ivan Vulić, Georgios P. Spithourakis, Paweł Budzianowski

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.

Natural Language Understanding

Improved and Efficient Conversational Slot Labeling through Question Answering

no code implementations5 Apr 2022 Gabor Fuisz, Ivan Vulić, Samuel Gibbons, Inigo Casanueva, Paweł Budzianowski

In particular, we focus on modeling and studying \textit{slot labeling} (SL), a crucial component of NLU for dialog, through the QA optics, aiming to improve both its performance and efficiency, and make it more effective and resilient to working with limited task data.

Natural Language Understanding Pretrained Language Models +1

Semi-supervised Bootstrapping of Dialogue State Trackers for Task Oriented Modelling

no code implementations26 Nov 2019 Bo-Hsiang Tseng, Marek Rei, Paweł Budzianowski, Richard E. Turner, Bill Byrne, Anna Korhonen

Dialogue systems benefit greatly from optimizing on detailed annotations, such as transcribed utterances, internal dialogue state representations and dialogue act labels.

Tree-Structured Semantic Encoder with Knowledge Sharing for Domain Adaptation in Natural Language Generation

no code implementations WS 2019 Bo-Hsiang Tseng, Paweł Budzianowski, Yen-chen Wu, Milica Gašić

Domain adaptation in natural language generation (NLG) remains challenging because of the high complexity of input semantics across domains and limited data of a target domain.

Domain Adaptation Informativeness +1

Domain Transfer in Dialogue Systems without Turn-Level Supervision

1 code implementation16 Sep 2019 Joachim Bingel, Victor Petrén Bach Hansen, Ana Valeria Gonzalez, Paweł Budzianowski, Isabelle Augenstein, Anders Søgaard

Task oriented dialogue systems rely heavily on specialized dialogue state tracking (DST) modules for dynamically predicting user intent throughout the conversation.

Dialogue State Tracking reinforcement-learning +1

Hello, It's GPT-2 -- How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems

no code implementations12 Jul 2019 Paweł Budzianowski, Ivan Vulić

Data scarcity is a long-standing and crucial challenge that hinders quick development of task-oriented dialogue systems across multiple domains: task-oriented dialogue models are expected to learn grammar, syntax, dialogue reasoning, decision making, and language generation from absurdly small amounts of task-specific data.

Decision Making Language Modelling +4

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

MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling

3 code implementations EMNLP 2018 Paweł Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng, Iñigo Casanueva, Stefan Ultes, Osman Ramadan, Milica Gašić

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.

Response Generation

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

Uncertainty Estimates for Efficient Neural Network-based Dialogue Policy Optimisation

no code implementations30 Nov 2017 Christopher Tegho, Paweł Budzianowski, Milica Gašić

This paper examines approaches to extract uncertainty estimates from deep Q-networks (DQN) in the context of dialogue management.

Dialogue Management Efficient Exploration +1

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