# FRACAS: A FRench Annotated Corpus of Attribution relations in newS

arXiv: https://arxiv.org/abs/2309.10604 · HTML: https://mlsanity.com/paper/2309.10604/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 52 references that are in the map.
- First posted on arXiv 19 Sep 2023.

## Builds on (most cited first, 13 of 13)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://mlsanity.com/paper/1909.03546/) (2019)
- [DocRED: A Large-Scale Document-Level Relation Extraction Dataset](https://mlsanity.com/paper/1906.06127/) (2019)
- [Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders](https://mlsanity.com/paper/2010.03851/) (2020)
- [DeepStruct: Pretraining of Language Models for Structure Prediction](https://mlsanity.com/paper/2205.10475/) (2022)
- [DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extraction](https://mlsanity.com/paper/2302.08675/) (2023)
- [DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population](https://mlsanity.com/paper/2201.03335/) (2022)
- [REDrm FM: a Filtered and Multilingual Relation Extraction Dataset](https://mlsanity.com/paper/2306.09802/) (2023)
- [SumREN: Summarizing Reported Speech about Events in News](https://mlsanity.com/paper/2212.01146/) (2022)
- [Exploration and Discovery of the COVID-19 Literature through Semantic Visualization](https://mlsanity.com/paper/2007.01800/) (2020)
- [Radar de Parit'e: An NLP system to measure gender representation in French news stories](https://mlsanity.com/paper/2304.09982/) (2023)

## Built on by (most cited first, 3 of 3)

- [COLE: a Comprehensive Benchmark for French Language Understanding Evaluation](https://mlsanity.com/paper/2510.05046/) (2025)
- [Hierarchical Text Classification with LLM-Refined Taxonomies](https://mlsanity.com/paper/2601.18375/) (2026)
- [SciNets: Graph-Constrained Multi-Hop Reasoning for Scientific Literature Synthesis](https://mlsanity.com/paper/2601.09727/) (2025)

## Abstract (arXiv)

Quotation extraction is a widely useful task both from a sociological and from a Natural Language Processing perspective. However, very little data is available to study this task in languages other than English. In this paper, we present a manually annotated corpus of 1676 newswire texts in French for quotation extraction and source attribution. We first describe the composition of our corpus and the choices that were made in selecting the data. We then detail the annotation guidelines and annotation process, as well as a few statistics about the final corpus and the obtained balance between quote types (direct, indirect and mixed, which are particularly challenging). We end by detailing our inter-annotator agreement between the 8 annotators who worked on manual labelling, which is substantially high for such a difficult linguistic phenomenon.