Search Results for author: Iñigo Casanueva

Found 15 papers, 7 papers with code

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

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.

Natural Language Understanding

Efficient Intent Detection with Dual Sentence Encoders

5 code implementations WS 2020 Iñigo Casanueva, Tadas Temčinas, Daniela Gerz, Matthew Henderson, Ivan Vulić

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).

Intent Detection Sentence

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 +2

Addressing Objects and Their Relations: The Conversational Entity Dialogue Model

no code implementations WS 2018 Stefan Ultes, Paweł\ Budzianowski, Iñigo Casanueva, Lina Rojas-Barahona, Bo-Hsiang Tseng, Yen-chen Wu, Steve Young, Milica Gašić

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.

Spoken Dialogue Systems

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

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.

Response Generation

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