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ding-01 :ARG0: An AMR Corpus for Spontaneous French Dialogue

arXiv 2508.12819 ↗First posted 18 Aug 2025cs.CLcited by 0

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Abstract (arXiv)

We present our work to build a French semantic corpus by annotating French dialogue in Abstract Meaning Representation (AMR). Specifically, we annotate the DinG corpus, consisting of transcripts of spontaneous French dialogues recorded during the board game Catan. As AMR has insufficient coverage of the dynamics of spontaneous speech, we extend the framework to better represent spontaneous speech and sentence structures specific to French. Additionally, to support consistent annotation, we provide an annotation guideline detailing these extensions. We publish our corpus under a free license (CC-SA-BY). We also train and evaluate an AMR parser on our data. This model can be used as an assistance annotation tool to provide initial annotations that can be refined by human annotators. Our work contributes to the development of semantic resources for French dialogue.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 27 references that are in the map. First posted on arXiv 18 Aug 2025.

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References · 7

  1. 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,035
  2. 2017Neural AMR: Sequence-to-Sequence Models for Parsing and Generationcited by 56
  3. 2018Toward Abstractive Summarization Using Semantic Representationscited by 43
  4. 2017Cross-lingual Abstract Meaning Representation Parsingcited by 19
  5. 2023"You Are An Expert Linguistic Annotator": Limits of LLMs as Analyzers of Abstract Meaning Representationcited by 15
  6. 2023Lost in Translationese? Reducing Translation Effect Using Abstract Meaning Representationcited by 10
  7. 2022A Multi-Party Dialogue Ressource in Frenchcited by 3

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