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

2025-08-18

AI Summary

Existing Abstract Meaning Representation (AMR) frameworks inadequately model spontaneous French dialogue due to insufficient handling of spoken-language dynamics (e.g., self-repairs, ellipsis, incomplete utterances) and poor coverage of French-specific syntactic phenomena (e.g., pro-drop, verb-second variants). Method: We construct the first AMR corpus for spontaneous French dialogue, systematically extending the AMR formalism with novel nodes and relations to encode dialogue acts, discourse repairs, and French morphosyntactic properties. We accompany this with a comprehensive bilingual annotation guideline. Contribution/Results: Leveraging this corpus, we train and evaluate the first French AMR parser, enabling semi-automatic annotation. All resources—including the corpus, guidelines, and parser—are released under the CC-BY-SA license. This work significantly advances AMR’s capacity to model informal spoken semantics and enhances its practical utility for French NLP.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Knowledge Representation and Reasoning: Common-Sense Reasoning

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

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.

Problem

• Building a French semantic corpus using AMR annotations
• Extending AMR framework for spontaneous French dialogue
• Training an AMR parser to assist annotation process

Innovation

• Extending AMR for spontaneous French dialogue
• Providing annotation guidelines for consistency
• Training AMR parser as annotation assistance