Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Casanueva, Inigo"'
Knowledge-based authentication is crucial for task-oriented spoken dialogue systems that offer personalised and privacy-focused services. Such systems should be able to enrol (E), verify (V), and identify (I) new and recurring users based on their pe
Externí odkaz:
http://arxiv.org/abs/2204.13496
We present NLU++, a novel dataset for natural language understanding (NLU) in task-oriented dialogue (ToD) systems, with the aim to provide a much more challenging evaluation environment for dialogue NLU models, up to date with the current applicatio
Externí odkaz:
http://arxiv.org/abs/2204.13021
Transformer-based pretrained language models (PLMs) offer unmatched performance across the majority of natural language understanding (NLU) tasks, including a body of question answering (QA) tasks. We hypothesize that improvements in QA methodology c
Externí odkaz:
http://arxiv.org/abs/2204.02123
Autor:
Vulić, Ivan, Su, Pei-Hao, Coope, Sam, Gerz, Daniela, Budzianowski, Paweł, Casanueva, Iñigo, Mrkšić, Nikola, Wen, Tsung-Hsien
Transformer-based language models (LMs) pretrained on large text collections are proven to store a wealth of semantic knowledge. However, 1) they are not effective as sentence encoders when used off-the-shelf, and 2) thus typically lag behind convers
Externí odkaz:
http://arxiv.org/abs/2109.10126
Building conversational systems in new domains and with added functionality requires resource-efficient models that work under low-data regimes (i.e., in few-shot setups). Motivated by these requirements, we introduce intent detection methods backed
Externí odkaz:
http://arxiv.org/abs/2003.04807
Autor:
Henderson, Matthew, Casanueva, Iñigo, Mrkšić, Nikola, Su, Pei-Hao, Wen, Tsung-Hsien, Vulić, Ivan
General-purpose pretrained sentence encoders such as BERT are not ideal for real-world conversational AI applications; they are computationally heavy, slow, and expensive to train. We propose ConveRT (Conversational Representations from Transformers)
Externí odkaz:
http://arxiv.org/abs/1911.03688
Autor:
Henderson, Matthew, Vulić, Ivan, Casanueva, Iñigo, Budzianowski, Paweł, Gerz, Daniela, Coope, Sam, Spithourakis, Georgios, Wen, Tsung-Hsien, Mrkšić, Nikola, Su, Pei-Hao
We present PolyResponse, a conversational search engine that supports task-oriented dialogue. It is a retrieval-based approach that bypasses the complex multi-component design of traditional task-oriented dialogue systems and the use of explicit sema
Externí odkaz:
http://arxiv.org/abs/1909.01296
Autor:
Henderson, Matthew, Vulić, Ivan, Gerz, Daniela, Casanueva, Iñigo, Budzianowski, Paweł, Coope, Sam, Spithourakis, Georgios, Wen, Tsung-Hsien, Mrkšić, Nikola, Su, Pei-Hao
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. Inspired
Externí odkaz:
http://arxiv.org/abs/1906.01543
Autor:
Henderson, Matthew, Budzianowski, Paweł, Casanueva, Iñigo, Coope, Sam, Gerz, Daniela, Kumar, Girish, Mrkšić, Nikola, Spithourakis, Georgios, Su, Pei-Hao, Vulić, Ivan, Wen, Tsung-Hsien
Publikováno v:
Proceedings of the Workshop on NLP for Conversational AI (2019)
Progress in Machine Learning is often driven by the availability of large datasets, and consistent evaluation metrics for comparing modeling approaches. To this end, we present a repository of conversational datasets consisting of hundreds of million
Externí odkaz:
http://arxiv.org/abs/1904.06472
Autor:
Ultes, Stefan, Budzianowski, Paweł\, Casanueva, Iñigo, Rojas-Barahona, Lina, Tseng, Bo-Hsiang, Wu, Yen-Chen, Young, Steve, Gašić, Milica
Statistical spoken dialogue systems usually rely on a single- or multi-domain dialogue model that is restricted in its capabilities of modelling complex dialogue structures, e.g., relations. In this work, we propose a novel dialogue model that is cen
Externí odkaz:
http://arxiv.org/abs/1901.01466