Abstract In Automated Essay Scoring (AES), benchmarking practices have fostered minimalist evaluation practices, in contrast with the broader-view recommendations of evaluation frameworks, such as the argument-based validation framework (ABV), which argued in favor of a multidimensional assessment of systems, especially in the context of high-stakes language tests. In this paper, we introduce an enhanced and more practical version of the ABV framework, incorporating fairness analysis, correlations with linguistic features, prediction error evaluation, and model agreement compared with human raters. Applying this framework to French AES, we compare 8 model architectures on a corpus of 27k exam essays (2 raters each) and a generalization corpus of 961 essays (at least nine raters each). Our analyses illustrate the benefits of applying the ABV framework to better understand the capabilities and pitfalls of AES models, while also advancing the state-of-the-art for French AES.

The present work studies the contribution of move structure to automatic genre identification. This concept - well known in other branches of genre analysis - seems to have little application in natural language processing. We describe how we collect a corpus of websites in French related to tourism and annotate it with move structure. We conduct experiments on automatic genre identification with our corpus. Our results show that our approach for informing a model with move structure can increase its performance for automatic genre identification, and reduce the need for annotated data and computational power.

Adrien Bibal, Rémi Cardon, David Alfter, Rodrigo Wilkens, Xiaoou Wang, Thomas François, Patrick Watrin. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

Rémi Cardon, Adrien Bibal, Rodrigo Wilkens, David Alfter, Magali Norré, Adeline Müller, Watrin Patrick, Thomas François. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.

We present experiments on biomedical text simplification in French.We use two kinds of corpora -parallel sentences extracted from existing health comparable corpora in French and WikiLarge corpus translated from English to French -and a lexicon that associates medical terms with paraphrases.Then, we train neural models on these parallel corpora using different ratios of general and specialized sentences.We evaluate the results with BLEU, SARI and Kandel scores.The results point out that little specialized data helps significantly the simplification.

Semantic similarity is an area of Natural Language Processing that is useful for several downstream applications, such as machine translation, natural language generation, information retrieval, or question answering. The task consists in assessing the extent to which two sentences express or do not express the same meaning. To do so, corpora with graded pairs of sentences are required. The grade is positioned on a given scale, usually going from 0 (completely unrelated) to 5 (equivalent semantics). In this work, we introduce such a corpus for French, the first that we know of. It is comprised of 1,010 sentence pairs with grades from five annotators. We describe the annotation process, analyse these data, and perform a few experiments for the automatic grading of semantic similarity.