ding-01 : ARG 0: An AMR Corpus for Spontaneous F rench Dialogue (2025)

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.

## introduction abstract meaning representation @xcite encodes the meaning of a text as a rooted, directed, and acyclic graph (see figure 1 ). representing meaning in a structured form offers several advantages for information systems. amr reduces semantic ambiguity by explicitly specifying one plausible interpretation among others. furthermore, because amr abstracts away from surface variations -especially syntactic variationssentences with the same underlying meaning share the same amr representation (e.g., "the police arrested the thief." and "the thief was arrested by the police."). this canonical representation reduces the search space for models, making amr a useful tool for various nlp tasks, such as machine translation @xcite , automatic text summarization @xcite , and human-robot interaction @xcite . training an amr parser to automatically generate an amr graph from a given text requires a dataset consisting of texts associated with their corresponding amr graphs. however, amr datasets for french are currently scarce, since most available amr resources are in english. this imbalance in semantic resources limits the development of french semantic parsers, which hinders the progress of french nlp systems that rely on them. furthermore, most existing amr data are based on written texts such as newspaper articles and online forums. in contrast, dialogue data, which exhibits unique linguistic features due to its interactive and spontaneous nature -e.g., french discourse markers such as alors (then), du coup (so), donc (so), and backchannels-remain underrepresented. to fill this gap in french semantic resources, particularly for dialogue, we manually annotate the ding corpus @xcite in amr. ding consists of transcriptions of dialogues recorded during board game sessions of catan, capturing various linguistic features of spoken interaction in french. however, the standard amr framework, as currently defined, foot_0 has limitations in representing speech-specific features. therefore, we extend amr by introducing additional relations to (i) annotate two pragmatic phenomena: discourse markers and backchannel expressions, (ii) represent coreference across multiple turns of speech. to summarize, our main contributions are as follows: we expect our corpus to contribute to the future development of semantic parsers for french dialogue, along with future (computational) linguistics research on french dialogical data. as noted by @xcite , amr corpora and tools are an underexplored source of data for linguistic investigation. the corpus is already getting some interest from the semantics research community, as it has been integrated in grew @xcite and can now be explored in the tool. 3 2 background and related work ## the ding corpus we annotate the ding corpus foot_6 @xcite , a collection of manually transcribed multi-party dialogues among french-speaking players of the board game catan. 8 catan is a strategic board game centered on resource management and exchange. thus, players often negotiate resource exchanges with each other, and their actual interactions are recorded in the corpus. we select this corpus for two main reasons. first, ding is available under a free license. 9 as our goal is to make our data public, selecting open data is a crucial requirement. second, ding consists of natural dialogues among speakers. since the environment is not controlled by the data collectors and the players are free to interact during the game, this dataset captures a natural conversational flow and includes a wide variety of dialogic phenomena. as such, its semantic annotations will serve as an ideal testbed for evaluating pre-trained language models on spontaneous speech transcriptions. ## ding-01 in this section, we present some statistics on the corpus, the annotation process, and the data quality assessed by inter-annotator agreement. the annotation was carried out over a six-months period, during which approximately 1,830 (see among these examples, there are 459 discourse markers and 36 instances of backchannel. the corpus was primarily annotated by the first author of this article using the metamorphosed annotation tool (heinecke, 2023, see figure 3 ). approximately 15% of the examples in the entire corpus were validated by two other annotators, who are coauthors of this article. specifically, the lead annotator and the two annotators met regularly throughout the annotation process (once a week or every two weeks) to check the validity of the examples one by one and record any difficulties encountered. in case of disagreement among the three annotators, the example was corrected or modified during the discussion. we encountered several challenges during the annotation process. one example concerned the word 'donc' (so), which appears frequently in ding. in most cases, it functions more as a discourse marker (used to start a speech turn or as a filler word) than as a causal connector. however, its usage was often ambiguous, and both interpretations could be valid depending on the context. to reduce ambiguity and improve consistency between annotations, we established the following rule: systematically annotate 'donc' as a discourse marker, provided that its removal does not change the meaning of the sentence. our method for addressing other similar challenges by defining clear directions is detailed in our annotation guidelines. furthermore, when faced with complex cases, or cases where multiple annotation choices were correct, we referred to existing amr 3.0 data in english to choose the most plausible annotation. these examples contain comments with references to the amr 3.0 sentences that justify these choices. to assess the quality of the annotations, 160 examples from our corpus were annotated by two annotators. the agreement score was measured using the smatch @xcite score. smatch is an evaluation metric for amr calculated by counting the number of triplets (node, labeled edge, node) in common. we obtained a score of 71.6. for comparison, @xcite reports inter-annotator agreement scores ranging from 71 to 83, depending on the data source and the annotators' level of expertise. after this evaluation, we performed an annotation conflict resolution step to produce our final gold corpus. all three authors jointly reviewed these 160 annotation examples. in cases of disagreement, the group resolved conflicts by choosing one of the existing annotations or agreeing on a new alternative. common conflicts involved edge labels such as :arg0, :arg1, and :arg2, typically resulting from annotation mistakes that were straightforward to correct once identified. another recurring issue concerned the selection of synonymous propbank concepts. for instance, own-01 and possess-01 convey the same meaning and share the same two semantic roles (:arg0 for the owner and :arg1 for the owned item). in the english amr data, the choice between these concepts is guided by the specific lexical item used in the sentence. we used these cases of conflict to refine our annotation guidelines, ensuring a consistent selection between such synonymous concepts. ## amr adapted for ding while adhering as closely as possible to standard amr, we introduce some extensions to better capture the specific features of spontaneous french speech. some of these key features are outlined below. in addition, we annotate inter-instance coreference, which is an addition that sets our corpus apart from amr 3.0. we also adapt the standard amr concept of focus to represent focalization strategies in spoken french. further details on these extensions are provided in our annotation guideline. for ding-01 use cases requiring compatibility with the english amr 3.0 corpus, these extensions are designed to be easily removable. ## models we train an amr parser on the previously described data to showcase its practical use. the trained model can assist in the annotation process in our future work. specifically, the model automatically annotates the data, which can then be manually refined by a human annotator. this semiautomatic approach is useful for scaling up data annotation. ## conclusion and future work we presented our ongoing work to annotate the ding corpus in amr to contribute to linguistic resources for french. to better represent the dynamics of spontaneous speech in the ding corpus, we adapted standard amr by introducing new semantic roles. we provide an annotation guideline detailing these adaptations, as well as a data statement containing metadata of ding-01. 14 to demonstrate a practical application of the dataset, we trained and evaluated an amr parser on our data. the resulting model can also serve as an annotation assistance tool, helping to accelerate the annotation process and scale up the semantic annotation process. in our future work, we aim to expand the annotated dataset to approximately 3,000 utterances. umr uniform meaning representation (umr) have been introduced in @xcite as an extension of amr to languages other than english, with the ambition of being used to "annotate the semantic content of a text in any language". umr is developed as amr with additional features, notably aspect, tense, modality, along with expanded ones, such as quantification & scope, and discourse relations. while umr appears as a very promising representation tool, we have not yet used it for our purposes. there is no french-umr dataset available for now, which makes evaluation difficult, especially for corpora with complex language phenomena such as ding. we plan to participate in the development of amr to umr translation tools, which should result in several silver french-umr corpora, paving the way for further meaning representation work. the additions we made to amr in order to annotate ding are a lighter version of some of the additional annotations needed for umr annotation; thus our annotation guidelines could also be of use for a middle step between amr and umr.