1 code implementation • 5 Jan 2024 • Paweł Budzianowski, Taras Sereda, Tomasz Cichy, Ivan Vulić
However, certain applications, such as assistive conversational systems, require natural and conversational speech generation tools that also operate efficiently in real time.
no code implementations • 16 Nov 2023 • Evgeniia Razumovskaia, Ivan Vulić, Pavle Marković, Tomasz Cichy, Qian Zheng, Tsung-Hsien Wen, Paweł Budzianowski
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.
no code implementations • 4 Jul 2023 • Guangzhi Sun, Chao Zhang, Ivan Vulić, Paweł Budzianowski, Philip C. Woodland
In this work, we propose a Knowledge-Aware Audio-Grounded generative slot-filling framework, termed KA2G, that focuses on few-shot and zero-shot slot filling for ToD with speech input.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +6
1 code implementation • Findings (NAACL) 2022 • Georgios P. Spithourakis, Ivan Vulić, Michał Lis, Iñigo Casanueva, Paweł Budzianowski
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
1 code implementation • Findings (NAACL) 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.
no code implementations • 5 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.
no code implementations • EMNLP 2021 • Ivan Vulić, Pei-Hao Su, Sam Coope, Daniela Gerz, Paweł Budzianowski, Iñigo Casanueva, Nikola Mrkšić, Tsung-Hsien Wen
Transformer-based language models (LMs) pretrained on large text collections are proven to store a wealth of semantic knowledge.
no code implementations • 26 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.
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.
1 code implementation • 16 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.
no code implementations • IJCNLP 2019 • Matthew Henderson, Ivan Vulić, Iñigo Casanueva, Paweł Budzianowski, Daniela Gerz, Sam Coope, Georgios Spithourakis, Tsung-Hsien Wen, Nikola Mrkšić, Pei-Hao Su
We present PolyResponse, a conversational search engine that supports task-oriented dialogue.
1 code implementation • 12 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.
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.
3 code implementations • WS 2019 • Matthew Henderson, Paweł Budzianowski, Iñigo Casanueva, Sam Coope, Daniela Gerz, Girish Kumar, Nikola Mrkšić, Georgios Spithourakis, Pei-Hao Su, Ivan Vulić, Tsung-Hsien Wen
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
5 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.
2 code implementations • ACL 2018 • Osman Ramadan, Paweł Budzianowski, Milica Gašić
Robust dialogue belief tracking is a key component in maintaining good quality dialogue systems.
Ranked #22 on Multi-domain Dialogue State Tracking on MULTIWOZ 2.0
no code implementations • NAACL 2018 • Iñigo Casanueva, Paweł Budzianowski, Pei-Hao Su, Stefan Ultes, Lina Rojas-Barahona, Bo-Hsiang Tseng, Milica Gašić
Reinforcement learning (RL) is a promising approach to solve dialogue policy optimisation.
no code implementations • 11 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.
no code implementations • 30 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.
no code implementations • 29 Nov 2017 • Iñigo Casanueva, Paweł Budzianowski, Pei-Hao Su, Nikola Mrkšić, Tsung-Hsien Wen, Stefan Ultes, Lina Rojas-Barahona, Steve Young, Milica Gašić
Dialogue assistants are rapidly becoming an indispensable daily aid.
no code implementations • WS 2017 • Stefan Ultes, Paweł Budzianowski, Iñigo Casanueva, Nikola Mrkšić, Lina Rojas-Barahona, Pei-Hao Su, Tsung-Hsien Wen, Milica Gašić, Steve Young
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
no code implementations • WS 2017 • Paweł Budzianowski, Stefan Ultes, Pei-Hao Su, Nikola Mrkšić, Tsung-Hsien Wen, Iñigo Casanueva, Lina Rojas-Barahona, Milica Gašić
In doing that, we show that our approach has the potential to facilitate policy optimisation for more sophisticated multi-domain dialogue systems.