n this paper, we present our contribution to the FinTOC-2022 Shared Task “Financial Document Structure Extraction”. We participated in the three tracks dedicated to English, French and Spanish document processing. Our main contribution consists in considering financial prospectus as a bundle of documents, i.e., a set of merged documents, each with their own layout and structure. Therefore, Document Layout and Structure Analysis (DLSA) first starts with the boundary detection of each document using general layout features. Then, the process applies inside each single document, taking advantage of the local properties. DLSA is achieved considering simultaneously text content, vectorial shapes and images embedded in the native PDF document. For the Title Detection task in English and French, we observed a significant improvement of the F-measures for Title Detection compared with those obtained during our previous participation.

G-LexAr est un analyseur morphologique de l’arabe qui a récemment reçu des améliorations substantielles. Cet article propose une évaluation de cet analyseur en tant qu’outil de pré-traitement pour la traduction automatique statistique, ce dont il n’a encore jamais fait l’objet. Nous étudions l’impact des différentes formes proposées par son analyse (voyellation, lemmatisation et segmentation) sur un système de traduction arabe-anglais, ainsi que l’impact de la combinaison de ces formes. Nos expériences montrent que l’utilisation séparée de chacune de ces formes n’a que peu d’influence sur la qualité des traductions obtenues, tandis que leur combinaison y contribue de façon très bénéfique.

In this paper we explore the contribution of the use of two Arabic morphological analyzers as preprocessing tools for statistical machine translation. Similar investigations have already been reported for morphologically rich languages like German, Turkish and Arabic. Here, we focus on the case of the Arabic language and mainly discuss the use of the G-LexAr analyzer. A preliminary experiment has been designed to choose the most promising translation system among the 3 G-LexAr-based systems, we concluded that the systems are equivalent. Nevertheless, we decided to use the lemmatized output of G-LexAr and use its translations as primary run for the BTEC AE track. The results showed that G-LexAr outputs degrades translation compared to the basic SMT system trained on the un-analyzed corpus.