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20152022
most citedSemantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints

38 citations · 83 across the 5 of their papers we have counts for

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8 papers · 1 filter

cs.CL2022★ 24 cited

Report from the NSF Future Directions Workshop on Automatic Evaluation of Dialog: Research Directions and Challenges

Shikib Mehri, Jinho Choi, Luis Fernando D'Haro +13

This is a report on the NSF Future Directions Workshop on Automatic Evaluation of Dialog. The workshop explored the current state of the art along with its limitations and suggeste…

cs.CL2018

Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems

Bo-Hsiang Tseng, Florian Kreyssig, Pawel Budzianowski +4

Cross-domain natural language generation (NLG) is still a difficult task within spoken dialogue modelling. Given a semantic representation provided by the dialogue manager, the lan…

cs.CL2018

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

Paweł Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng +4

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. To address this funda…

cs.CL2018

Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing

Osman Ramadan, Paweł Budzianowski, Milica Gašić

Robust dialogue belief tracking is a key component in maintaining good quality dialogue systems. The tasks that dialogue systems are trying to solve are becoming increasingly compl…

cs.CL2018

Neural User Simulation for Corpus-based Policy Optimisation for Spoken Dialogue Systems

Florian Kreyssig, Inigo Casanueva, Pawel Budzianowski +1

User Simulators are one of the major tools that enable offline training of task-oriented dialogue systems. For this task the Agenda-Based User Simulator (ABUS) is often used. The A…

cs.CL2017★ 13 cited

Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management

Pei-Hao Su, Pawel Budzianowski, Stefan Ultes +2

Deep reinforcement learning (RL) methods have significant potential for dialogue policy optimisation. However, they suffer from a poor performance in the early stages of learning.…