Achraf Boumhidi

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2024

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NLP-LISAC at SemEval-2024 Task 1: Transformer-based approaches for Determining Semantic Textual Relatedness
Abdessamad Benlahbib | Anass Fahfouh | Hamza Alami | Achraf Boumhidi
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)

This paper presents our system and findings for SemEval 2024 Task 1 Track A Supervised Semantic Textual Relatedness. The main objective of this task was to detect the degree of semantic relatedness between pairs of sentences. Our submitted models (ranked 6/24 in Algerian Arabic, 7/25 in Spanish, 12/23 in Moroccan Arabic, and 13/36 in English) consist of various transformer-based models including MARBERT-V2, mDeBERTa-V3-Base, DarijaBERT, and DeBERTa-V3-Large, fine-tuned using different loss functions including Huber Loss, Mean Absolute Error, and Mean Squared Error.

2023

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NLP-LISAC at SemEval-2023 Task 9: Multilingual Tweet Intimacy Analysis via a Transformer-based Approach and Data Augmentation
Abdessamad Benlahbib | Hamza Alami | Achraf Boumhidi | Omar Benslimane
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

This paper presents our system and findings for SemEval 2023 Task 9 Tweet Intimacy Analysis. The main objective of this task was to predict the intimacy of tweets in 10 languages. Our submitted model (ranked 28/45) consists of a transformer-based approach with data augmentation via machine translation.

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NLP-LISAC at SemEval-2023 Task 12: Sentiment Analysis for Tweets expressed in African languages via Transformer-based Models
Abdessamad Benlahbib | Achraf Boumhidi
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

This paper presents our systems and findings for SemEval-2023 Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages. The main objective of this task was to determine the polarity of a tweet (positive, negative, or neutral). Our submitted models (highest rank is 1 and lowest rank is 21 depending on the target Track) consist of various Transformer-based approaches.
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