no code implementations • NoDaLiDa 2021 • Joakim Olsen, Arild Brandrud Næss, Pierre Lison
This paper explores how to automatically measure the quality of human-generated summaries, based on a Norwegian corpus of real estate condition reports and their corresponding summaries.
no code implementations • 3 Nov 2023 • Nicholas Thomas Walker, Stefan Ultes, Pierre Lison
After this conversion, the text representation of the dialogue state graph is included as part of the prompt of a large language model used to decode the agent response.
no code implementations • 22 Oct 2023 • Anthi Papadopoulou, Pierre Lison, Mark Anderson, Lilja Øvrelid, Ildikó Pilán
The text sanitization process starts with a privacy-oriented entity recognizer that seeks to determine the text spans expressing identifiable personal information.
no code implementations • 20 Oct 2023 • Nicholas Thomas Walker, Stefan Ultes, Pierre Lison
Constructing responses in task-oriented dialogue systems typically relies on information sources such the current dialogue state or external databases.
no code implementations • 27 Sep 2023 • Ildikó Pilán, Laurent Prévot, Hendrik Buschmeier, Pierre Lison
This difference is particularly marked for communicative feedback and grounding phenomena such as backchannels, acknowledgments, or clarification requests.
no code implementations • 30 Apr 2023 • Anders Mølmen Høst, Pierre Lison, Leon Moonen
Knowledge graphs have shown promise for several cybersecurity tasks, such as vulnerability assessment and threat analysis.
no code implementations • 15 Mar 2023 • Pierre Lison, Casey Kennington
Software architectures for conversational robots typically consist of multiple modules, each designed for a particular processing task or functionality.
1 code implementation • 23 Nov 2022 • Nicholas Thomas Walker, Stefan Ultes, Pierre Lison
We present a new approach to dialogue management using conversational knowledge graphs as core representation of the dialogue state.
1 code implementation • LREC 2022 • Anthi Papadopoulou, Pierre Lison, Lilja Øvrelid, Ildikó Pilán
Instead of requiring manually labeled training data, the approach relies on a knowledge graph expressing the background information assumed to be publicly available about various individuals.
2 code implementations • 25 Jan 2022 • Ildikó Pilán, Pierre Lison, Lilja Øvrelid, Anthi Papadopoulou, David Sánchez, Montserrat Batet
We present a novel benchmark and associated evaluation metrics for assessing the performance of text anonymization methods.
1 code implementation • ACL 2021 • Pierre Lison, Ildik{\'o} Pil{\'a}n, David Sanchez, Montserrat Batet, Lilja {\O}vrelid
This position paper investigates the problem of automated text anonymisation, which is a prerequisite for secure sharing of documents containing sensitive information about individuals.
1 code implementation • ACL 2021 • Pierre Lison, Jeremy Barnes, Aliaksandr Hubin
skweak is especially designed to facilitate the use of weak supervision for NLP tasks such as text classification and sequence labelling.
1 code implementation • ACL 2020 • Pierre Lison, Aliaksandr Hubin, Jeremy Barnes, Samia Touileb
When in-domain labelled data is available, transfer learning techniques can be used to adapt existing NER models to the target domain.
no code implementations • IJCNLP 2019 • Youngsoo Jang, Jongmin Lee, Jaeyoung Park, Kyeng-Hun Lee, Pierre Lison, Kee-Eung Kim
We present PyOpenDial, a Python-based domain-independent, open-source toolkit for spoken dialogue systems.
no code implementations • WS 2017 • Pierre Lison, Serge Bibauw
Neural conversational models require substantial amounts of dialogue data for their parameter estimation and are therefore usually learned on large corpora such as chat forums or movie subtitles.
no code implementations • WS 2017 • Pierre Lison, Andrey Kutuzov
Distributional semantic models learn vector representations of words through the contexts they occur in.
no code implementations • LREC 2016 • Pierre Lison, J{\"o}rg Tiedemann
We present a new major release of the OpenSubtitles collection of parallel corpora.
no code implementations • 5 Apr 2013 • Pierre Lison
Reinforcement learning methods are increasingly used to optimise dialogue policies from experience.