Aria Nourbakhsh


2023

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The Dark Side of the Language: Pre-trained Transformers in the DarkNet
Leonardo Ranaldi | Aria Nourbakhsh | Elena Sofia Ruzzetti | Arianna Patrizi | Dario Onorati | Michele Mastromattei | Francesca Fallucchi | Fabio Massimo Zanzotto
Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing

Pre-trained Transformers are challenging human performances in many Natural Language Processing tasks. The massive datasets used for pre-training seem to be the key to their success on existing tasks. In this paper, we explore how a range of pre-trained natural language understanding models performs on definitely unseen sentences provided by classification tasks over a DarkNet corpus. Surprisingly, results show that syntactic and lexical neural networks perform on par with pre-trained Transformers even after fine-tuning. Only after what we call extreme domain adaptation, that is, retraining with the masked language model task on all the novel corpus, pre-trained Transformers reach their standard high results. This suggests that huge pre-training corpora may give Transformers unexpected help since they are exposed to many of the possible sentences.

2019

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sthruggle at SemEval-2019 Task 5: An Ensemble Approach to Hate Speech Detection
Aria Nourbakhsh | Frida Vermeer | Gijs Wiltvank | Rob van der Goot
Proceedings of the 13th International Workshop on Semantic Evaluation

In this paper, we present our approach to detection of hate speech against women and immigrants in tweets for our participation in the SemEval-2019 Task 5. We trained an SVM and an RF classifier using character bi- and trigram features and a BiLSTM pre-initialized with external word embeddings. We combined the predictions of the SVM, RF and BiLSTM in two different ensemble models. The first was a majority vote of the binary values, and the second used the average of the confidence scores. For development, we got the highest accuracy (75%) by the final ensemble model with majority voting. For testing, all models scored substantially lower and the scores between the classifiers varied more. We believe that these large differences between the higher accuracies in the development phase and the lower accuracies we obtained in the testing phase have partly to do with differences between the training, development and testing data.

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Toward Dialogue Modeling: A Semantic Annotation Scheme for Questions and Answers
María Andrea Cruz Blandón | Gosse Minnema | Aria Nourbakhsh | Maria Boritchev | Maxime Amblard
Proceedings of the 13th Linguistic Annotation Workshop

The present study proposes an annotation scheme for classifying the content and discourse contribution of question-answer pairs. We propose detailed guidelines for using the scheme and apply them to dialogues in English, Spanish, and Dutch. Finally, we report on initial machine learning experiments for automatic annotation.