Walid Aransa


2023

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FiRC at SemEval-2023 Task 10: Fine-grained Classification of Online Sexism Content Using DeBERTa
Fadi Hassan | Abdessalam Bouchekif | Walid Aransa
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

The SemEval 2023 shared task 10 “Explainable Detection of Online Sexism” focuses on detecting and identifying comments and tweets containing sexist expressions and also explaining why it is sexist. This paper describes our system that we used to participate in this shared task. Our model is an ensemble of different variants of fine tuned DeBERTa models that employs a k-fold cross-validation. We have participated in the three tasks A, B and C. Our model ranked 2 nd position in tasks A, 7 th in task B and 4 th in task C.

2017

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LIUM Machine Translation Systems for WMT17 News Translation Task
Mercedes García-Martínez | Ozan Caglayan | Walid Aransa | Adrien Bardet | Fethi Bougares | Loïc Barrault
Proceedings of the Second Conference on Machine Translation

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LIUM-CVC Submissions for WMT17 Multimodal Translation Task
Ozan Caglayan | Walid Aransa | Adrien Bardet | Mercedes García-Martínez | Fethi Bougares | Loïc Barrault | Marc Masana | Luis Herranz | Joost van de Weijer
Proceedings of the Second Conference on Machine Translation

2016

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Does Multimodality Help Human and Machine for Translation and Image Captioning?
Ozan Caglayan | Walid Aransa | Yaxing Wang | Marc Masana | Mercedes García-Martínez | Fethi Bougares | Loïc Barrault | Joost van de Weijer
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

2015

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Improving continuous space language models auxiliary features
Walid Aransa | Holger Schwenk | Loïc Barrault
Proceedings of the 12th International Workshop on Spoken Language Translation: Papers

2013

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A Multi-Domain Translation Model Framework for Statistical Machine Translation
Rico Sennrich | Holger Schwenk | Walid Aransa
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2012

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Semi-supervised transliteration mining from parallel and comparable corpora
Walid Aransa | Holger Schwenk | Loic Barrault
Proceedings of the 9th International Workshop on Spoken Language Translation: Papers

Transliteration is the process of writing a word (mainly proper noun) from one language in the alphabet of another language. This process requires mapping the pronunciation of the word from the source language to the closest possible pronunciation in the target language. In this paper we introduce a new semi-supervised transliteration mining method for parallel and comparable corpora. The method is mainly based on a new suggested Three Levels of Similarity (TLS) scores to extract the transliteration pairs. The first level calculates the similarity of of all vowel letters and consonants letters. The second level calculates the similarity of long vowels and vowel letters at beginning and end position of the words and consonants letters. The third level calculates the similarity consonants letters only. We applied our method on Arabic-English parallel and comparable corpora. We evaluated the extracted transliteration pairs using a statistical based transliteration system. This system is built using letters instead or words as tokens. The transliteration system achieves an accuracy of 0.50 and a mean F-score 0.8958 when trained on transliteration pairs extracted from a parallel corpus. The accuracy is 0.30 and the mean F-score 0.84 when we used instead a comparable corpus to automatically extract the transliteration pairs. This shows that the proposed semi-supervised transliteration mining algorithm is effective and can be applied to other language pairs. We also evaluated two segmentation techniques and reported the impact on the transliteration performance.