Zobrazeno 1 - 10
of 153
pro vyhledávání: '"AMBLARD, Maxime"'
Autor:
Decker, Amandine, Amblard, Maxime
Publikováno v:
CODI 2024 - 5th workshop on Computational Approaches to Discourse, Mar 2024, Malta, Malta
Topics play an important role in the global organisation of a conversation as what is currently discussed constrains the possible contributions of the participant. Understanding the way topics are organised in interaction would provide insight on the
Externí odkaz:
http://arxiv.org/abs/2402.02837
Publikováno v:
Findings of the Association for Computational Linguistics: EACL 2023 (2023) 2562--2579
Discourse processing suffers from data sparsity, especially for dialogues. As a result, we explore approaches to build discourse structures for dialogues, based on attention matrices from Pre-trained Language Models (PLMs). We investigate multiple ta
Externí odkaz:
http://arxiv.org/abs/2302.05895
Publikováno v:
f ISA-18 Workshop at LREC2022, Jun 2022, Marseille, France
In this paper, we consider two of the currently popular semantic frameworks: Abstract Meaning Representation (AMR)a more abstract framework, and Universal Conceptual Cognitive Annotation (UCCA)-an anchored framework. We use a corpus-based approach to
Externí odkaz:
http://arxiv.org/abs/2207.12174
Publikováno v:
f ISA-18 Workshop at LREC2022, Jun 2022, Marseille, France
This paper presents how the online tool GREW-MATCH can be used to make queries and visualise data from existing semantically annotated corpora. A dedicated syntax is available to construct simple to complex queries and execute them against a corpus.
Externí odkaz:
http://arxiv.org/abs/2207.12166
Autor:
Boritchev, Maria, Amblard, Maxime
Publikováno v:
13th Edition of Language Resources and Evaluation Conference (LREC 2022), Jun 2022, Marseille, France
We present Dialogues in Games (DinG), a corpus of manual transcriptions of real-life, oral, spontaneous multi-party dialogues between French-speaking players of the board game Catan. Our objective is to make available a quality resource for French, c
Externí odkaz:
http://arxiv.org/abs/2207.12162
Publikováno v:
The 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL 2022), Sep 2022, Edinbourg, United Kingdom
Depression is a serious mental illness that impacts the way people communicate, especially through their emotions, and, allegedly, the way they interact with others. This work examines depression signals in dialogs, a less studied setting that suffer
Externí odkaz:
http://arxiv.org/abs/2208.10250
Unintended biases in machine learning (ML) models are among the major concerns that must be addressed to maintain public trust in ML. In this paper, we address process fairness of ML models that consists in reducing the dependence of models on sensit
Externí odkaz:
http://arxiv.org/abs/2108.02662
Autor:
Cruz-Blandón, Maria-Andrea, Minnema, Gosse, Nourbakhsh, Aria, Boritchev, Maria, Amblard, Maxime
Publikováno v:
LAW XIII 2019 - Linguistic Annotation Workshop - ACL Workshop, Jul 2019, Florence, Italy
The present study proposes an annotation scheme for classifying the content and discourse contribution of question-answer pairs. We propose detailed guidelines for using the scheme and apply them to dialogues in English, Spanish, and Dutch. Finally,
Externí odkaz:
http://arxiv.org/abs/1908.09921