Marco DeGemmis


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XL-LEXEME: WiC Pretrained Model for Cross-Lingual LEXical sEMantic changE
Pierluigi Cassotti | Lucia Siciliani | Marco DeGemmis | Giovanni Semeraro | Pierpaolo Basile
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

The recent introduction of large-scale datasets for the WiC (Word in Context) task enables the creation of more reliable and meaningful contextualized word embeddings.However, most of the approaches to the WiC task use cross-encoders, which prevent the possibility of deriving comparable word embeddings.In this work, we introduce XL-LEXEME, a Lexical Semantic Change Detection model.XL-LEXEME extends SBERT, highlighting the target word in the sentence. We evaluate XL-LEXEME on the multilingual benchmarks for SemEval-2020 Task 1 - Lexical Semantic Change (LSC) Detection and the RuShiftEval shared task involving five languages: English, German, Swedish, Latin, and Russian.XL-LEXEME outperforms the state-of-the-art in English, German and Swedish with statistically significant differences from the baseline results and obtains state-of-the-art performance in the RuShiftEval shared task.


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swapUNIBA@FinTOC2022: Fine-tuning Pre-trained Document Image Analysis Model for Title Detection on the Financial Domain
Pierluigi Cassotti | Cataldo Musto | Marco DeGemmis | Georgios Lekkas | Giovanni Semeraro
Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022

In this paper, we introduce the results of our submitted system to the FinTOC 2022 task. We address the task using a two-stage process: first, we detect titles using Document Image Analysis, then we train a supervised model for the hierarchical level prediction. We perform Document Image Analysis using a pre-trained Faster R-CNN on the PublyaNet dataset. We fine-tuned the model on the FinTOC 2022 training set. We extract orthographic and layout features from detected titles and use them to train a Random Forest model to predict the title level. The proposed system ranked #1 on both Title Detection and the Table of Content extraction tasks for Spanish. The system ranked #3 on both the two subtasks for English and French.