This paper addresses a crucial yet understudied issue in argumentation studies: the distinction between explanations and justifications, and their interaction with subjectivity. Building on insights from Bex and Walton (2016), who highlight the importance of not conflating explanations with arguments, we propose a corpus-based approach to operationalize this distinction in French. We present FreCaDiS (French Corpus of Causal Connectives, Discourse Relations, and Subjectivity), a novel corpus of French texts annotated for explanatory and justificatory discourse relations and their perceived subjectivity. FreCaDiS comprises excerpts of 2–3 sentences drawn from five distinct genres—SMS, online discussions, blogs, press, and contemporary literature—spanning informal to formal registers. Specifically, we focus on sentences introduced by the connectives parce que and car (“because”) and annotate them along two dimensions: (i) discourse relation (explanation vs. justification) and (ii) subjectivity (subjective vs. objective). The corpus was annotated by three independent human annotators using complementary approaches: a holistic, an intuitive method for subjectivity and a guided, operationalized method for discourse relations. FreCaDiS provides a rich resource for the study of argumentation, causal discourse, causal connectives, and subjective interpretation in French and can support future work in computational argument mining, discourse analysis, and NLP applications.

This paper proposes a method for improving the results of a statistical Machine Translation system using boundedness, a pragmatic component of the verbal phrase’s lexical aspect. First, the paper presents manual and automatic annotation experiments for lexical aspect in EnglishFrench parallel corpora. It will be shown that this aspectual property is identified and classified with ease both by humans and by automatic systems. Second, Statistical Machine Translation experiments using the boundedness annotations are presented. These experiments show that the information regarding lexical aspect is useful to improve the output of a Machine Translation system in terms of better choices of verbal tenses in the target language, as well as better lexical choices. Ultimately, this work aims at providing a method for the automatic annotation of data with boundedness information and at contributing to Machine Translation by taking into account linguistic data.

This paper presents manual and automatic annotation experiments for a pragmatic verb tense feature (narrativity) in English/French parallel corpora. The feature is considered to play an important role for translating English Simple Past tense into French, where three different tenses are available. Whether the French Passe ́ Compose ́, Passe ́ Simple or Imparfait should be used is highly dependent on a longer-range context, in which either narrative events ordered in time or mere non-narrative state of affairs in the past are described. This longer-range context is usually not available to current machine translation (MT) systems, that are trained on parallel corpora. Annotating narrativity prior to translation is therefore likely to help current MT systems. Our experiments show that narrativity can be reliably identified with kappa-values of up to 0.91 in manual annotation and with F1 scores of up to 0.72 in automatic annotation.