Papers by Paweł Budzianowski
EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification (2022.findings-naacl)
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| Challenge: | Knowledge-based authentication is crucial for task-oriented spoken dialogue systems that offer personalised and privacy-focused services . e-learning systems should be able to enrol, identify, and verify new and recurring users based on their personal information . |
| Approach: | They propose to formalise three authentication tasks and their evaluation protocols . they propose to use a spoken multilingual dataset with 5,506 spoken dialogues . |
| Outcome: | The proposed models set the first competitive benchmarks and set directions for future research. |
Multi-Label Intent Detection via Contrastive Task Specialization of Sentence Encoders (2022.emnlp-main)
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Ivan Vulić, Iñigo Casanueva, Georgios Spithourakis, Avishek Mondal, Tsung-Hsien Wen, Paweł Budzianowski
| Challenge: | a novel framework for task-oriented dialog ToD systems is proposed . a task of recognizing the user's intent or goal from their utterance is a crucial component of any TOD system. |
| Approach: | They propose to transform general-purpose sentences into task-specialized SEs by contrastive fine-tuning on annotated multi-label data. |
| Outcome: | The proposed framework yields effective mID models with large gains over non-specialized models across a spectrum of different m ID datasets. |
Training Neural Response Selection for Task-Oriented Dialogue Systems (P19-1)
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Matthew Henderson, Ivan Vulić, Daniela Gerz, Iñigo Casanueva, Paweł Budzianowski, Sam Coope, Georgios Spithourakis, Tsung-Hsien Wen, Nikola Mrkšić, Pei-Hao Su
| Challenge: | Despite their popularity, retrieval-based models have had modest impact on task-oriented dialogue systems . main obstacle to their application is the low-data regime of most task-orientated dialogue tasks . e-commerce, banking, and other domains are applications of retrieval models . |
| Approach: | They propose a method which pretrains a retrieval-based model on large general-domain conversational corpora and fine-tunes it for the target dialogue domain. |
| Outcome: | The proposed method is evaluated on five diverse domains, ranging from e-commerce to banking. |
Hello, It’s GPT-2 - How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems (D19-56)
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| Challenge: | Statistical conversational systems are complex, timeintensive, expensive, and not easily transferable due to data scarcity. |
| Approach: | They propose a task-oriented dialogue model that operates on text input . they validate it on multi-domain task-orientated dialogues from a multi-word dataset . |
| Outcome: | The proposed model bypasses explicit policy and language generation modules on multi-domain task-oriented dialogues from the MultiWOZ dataset. |
Wait, that’s not an option: LLMs Robustness with Incorrect Multiple-Choice Options (2025.acl-long)
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| Challenge: | Using a framework that combines instruction-following with critical reasoning, we show that the ability of LLMs to override defaults when faced with invalid options is impaired by alignment techniques. |
| Approach: | They propose a framework for evaluating LLMs’ capacity to balance instruction-following with critical reasoning when presented with multiple-choice questions containing no valid answers. |
| Outcome: | The proposed framework improves models' ability to override defaults when faced with invalid options while minimizing the impact of model size and training techniques on the model. |
Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing (P18-2)
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| Challenge: | Existing approaches to scalability of dialogue belief tracking are dependent on the ontology of the dialogue . current approaches are not scalable to multi-domain dialogues because of the effort required to define a semantic dictionary for each domain. |
| Approach: | They propose a model that utilizes semantic similarity between dialogue utterances and ontology terms to allow information to be shared across domains. |
| Outcome: | The proposed model outperforms state-of-the-art models in multi-domain dialogue tracking tasks while maintaining high quality. |
Data Collection and End-to-End Learning for Conversational AI (D19-2)
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| Challenge: | tutorial aims to familiarise research community with recent advances in statistical dialogue systems . focus of tutorial is on learning end-to-end from data and their relation to more common modular systems. |
| Approach: | This tutorial aims to familiarise the research community with the latest advances in statistical dialogue systems . the focus of the tutorial is on recently introduced end-to-end learning for dialogue systems and their relation to more common modular systems. |
| Outcome: | This tutorial aims to familiarise the research community with the recent advances in statistical dialogue systems for open-domain and task-based dialogue paradigms. |
MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling (D18-1)
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Paweł Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng, Iñigo Casanueva, Stefan Ultes, Osman Ramadan, Milica Gašić
| Challenge: | a dataset of 10k human-human written conversations is one order of magnitude larger than previous annotated task-oriented corpora. |
| Approach: | They propose to collect 10k human-human written conversations from a crowd-sourced dataset using crowd-sourcing. |
| Outcome: | The proposed dataset is one order of magnitude larger than previous annotated task-oriented corpora and shows the usability of the data and sets a baseline for future studies. |
Dial BeInfo for Faithfulness: Improving Factuality of Information-Seeking Dialogue via Behavioural Fine-Tuning (2024.findings-emnlp)
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Evgeniia Razumovskaia, Ivan Vulić, Pavle Marković, Tomasz Cichy, Qian Zheng, Tsung-Hsien Wen, Paweł Budzianowski
| Challenge: | Pretrained large language models (LLMs) are prone to hallucinations, that is, they generate incoherent or generic responses to queries. |
| Approach: | They propose a method that applies 'behavioural tuning' on the LLMs to aid information-seeking dialogue by comparing three standard datasets. |
| Outcome: | The proposed method improves accuracy on real-life conversations with real users by allowing the models to perform better on the data. |
Feudal Reinforcement Learning for Dialogue Management in Large Domains (N18-2)
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Iñigo Casanueva, Paweł Budzianowski, Pei-Hao Su, Stefan Ultes, Lina M. Rojas-Barahona, Bo-Hsiang Tseng, Milica Gašić
| Challenge: | Reinforcement learning (RL) is a promising approach to model dialogue policy optimisation but fails to scale to large domains due to the curse of dimensionality. |
| Approach: | They propose a novel approach to dialogue policy optimisation using reinforcement learning . they propose to decompose the decision into two steps using a domain ontology . |
| Outcome: | The proposed architecture outperforms state-of-the-art in several dialogue domains without any additional reward signal. |
PolyResponse: A Rank-based Approach to Task-Oriented Dialogue with Application in Restaurant Search and Booking (D19-3)
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Matthew Henderson, Ivan Vulić, Iñigo Casanueva, Paweł Budzianowski, Daniela Gerz, Sam Coope, Georgios Spithourakis, Tsung-Hsien Wen, Nikola Mrkšić, Pei-Hao Su
| Challenge: | a task-oriented dialogue system is based on task-specific ontologies that constrain slots to specific values . we present a conversational search engine that can be used to search for restaurant reservations . |
| Approach: | They propose a conversational search engine that supports task-oriented dialogue . the polyresponse engine is trained on hundreds of millions of examples extracted from real conversations . |
| Outcome: | The proposed system is available in 8 different languages. |
Semi-Supervised Bootstrapping of Dialogue State Trackers for Task-Oriented Modelling (D19-1)
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| Challenge: | Existing systems rely on modular, domain-focused frameworks for analyzing complex problems. |
| Approach: | They propose semi-supervised learning methods that can reduce the amount of required intermediate labelling by leveraging un-annotated data instead of transcribed utterances. |
| Outcome: | The proposed model reduces the amount of turn-level annotations by 30% while maintaining equivalent system performance. |
ConvFiT: Conversational Fine-Tuning of Pretrained Language Models (2021.emnlp-main)
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Ivan Vulić, Pei-Hao Su, Samuel Coope, Daniela Gerz, Paweł Budzianowski, Iñigo Casanueva, Nikola Mrkšić, Tsung-Hsien Wen
| Challenge: | Existing Transformer-based language models (LMs) are not effective as sentence encoders when used off-the-shelf. |
| Approach: | They propose a method which turns a pretrained LM into a universal conversational encoder and task-specialised sentence encoder. |
| Outcome: | The proposed framework achieves state-of-the-art ID performance across the board with particular gains in the most challenging, few-shot setups. |
NLU++: A Multi-Label, Slot-Rich, Generalisable Dataset for Natural Language Understanding in Task-Oriented Dialogue (2022.findings-naacl)
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| Challenge: | NLU++ provides a more challenging evaluation environment for dialogue NLU models . Typical ToD systems still rely on a modular design . |
| Approach: | They propose to use NLU++ to provide a more challenging evaluation environment for dialogue NLU models. |
| Outcome: | The proposed dataset improves existing datasets and provides a much more challenging evaluation environment for dialogue NLU models. |