Papers by Iñigo Casanueva
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. |
Deep Learning for Conversational AI (N18-6)
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| Challenge: | Spoken Dialogue Systems (SDS) have great commercial potential . the advent of deep learning has led to significant advances in this area of NLP research . |
| Approach: | This tutorial will introduce researchers to the pipeline framework for modelling goal-oriented dialogue systems. |
| Outcome: | This tutorial will familiarise researchers with the latest advances in spoken dialogue systems . the aim of the course is to encourage dialogue research in the NLP community . |
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. |
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. |
ConveRT: Efficient and Accurate Conversational Representations from Transformers (2020.findings-emnlp)
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| Challenge: | ConveRT is a pretraining framework for conversational AI that is computationally heavy, slow, and expensive to train. |
| Approach: | They propose a pretraining framework for conversational tasks that is efficient, lightweight, and inexpensive. |
| Outcome: | The proposed model achieves state-of-the-art performance across widely established responses . it trains substantially faster than existing state- of-the art models . |
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. |
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. |