Papers by Maxime Amblard
Quantification Annotation in ISO 24617-12, Second Draft (2022.lrec-1)
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Harry Bunt, Maxime Amblard, Johan Bos, Karën Fort, Bruno Guillaume, Philippe de Groote, Chuyuan Li, Pierre Ludmann, Michel Musiol, Siyana Pavlova, Guy Perrier, Sylvain Pogodalla
| Challenge: | a project aimed at establishing an interoperable annotation schema for quantification phenomena was relaunched in early 2022 due to the Covid-19 pandemic . |
| Approach: | This paper describes the continuation of a project that aims at establishing an interoperable annotation schema for quantification phenomena as part of the ISO suite of semantic annotation standards. |
| Outcome: | The proposed schema is part of the ISO suite of semantic annotation standards known as the Semantic Annotation Framework (SemAF). |
A Multi-Party Dialogue Ressource in French (2022.lrec-1)
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| Challenge: | a corpus of manual transcriptions of real-life, oral, spontaneous multi-party dialogues is available for French-speaking players of the board game Catan. |
| Approach: | They propose to make available a corpus of manual transcriptions of real-life, oral, spontaneous multi-party dialogues between french-speaking players of the board game Catan. |
| Outcome: | The proposed corpus is composed of long human-human interactions and can be used for dialogue studies in many fields. |
Discourse Structure Extraction from Pre-Trained and Fine-Tuned Language Models in Dialogues (2023.findings-eacl)
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| Challenge: | Discourse processing suffers from data sparsity, especially for dialogues . a variety of discourse frameworks have been proposed to extract discourse information from dialogues. |
| Approach: | They propose unsupervised and semi-supervised methods to infer latent discourse structures for dialogues based on attention matrices from Pre-trained Language Models. |
| Outcome: | The proposed methods achieve encouraging results on the STAC corpus, with F1 scores of 57.2 and 59.3 for the unsupervised and semi-supervised methods, respectively. |
A French Version of the FraCaS Test Suite (2020.lrec-1)
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| Challenge: | a French version of the FraCaS test suite is presented in this paper . it contains problems illustrating semantic inference in natural language . |
| Approach: | They propose to test the NLP system's semantic capacity against inferencing tasks by translating the FraCaS test suite into French and running an experiment to test both the translation and the logical semantics underlying the problems. |
| Outcome: | The proposed tests were compared with similar tests conducted in other languages and show that the results are comparable to those of other tests. |
A Classifier of Word-Level Variants in Witnesses of Biblical Hebrew Manuscripts (2025.findings-acl)
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| Challenge: | a strong classifier (F1 value of 0.80) is trained to predict the category of difference between word pairs as present in collated (aligned) pairs of witnesses. |
| Approach: | The project is based on the relationship between available witnesses of biblical Hebrew and a strong classifier (F1 value of 0.80) is trained to predict the category of difference between word pairs as present in collated pairs of witnesses. |
| Outcome: | The proposed model is non-neural and uses part-of-speech tags, hand-crafted rules per category and synthetically derived data. |