Suzana Ilic

Also published as: Suzana Ilić


2022

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Proceedings of BigScience Episode #5 -- Workshop on Challenges & Perspectives in Creating Large Language Models
Angela Fan | Suzana Ilic | Thomas Wolf | Matthias Gallé
Proceedings of BigScience Episode #5 -- Workshop on Challenges & Perspectives in Creating Large Language Models

2018

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IIIDYT at SemEval-2018 Task 3: Irony detection in English tweets
Edison Marrese-Taylor | Suzana Ilic | Jorge Balazs | Helmut Prendinger | Yutaka Matsuo
Proceedings of The 12th International Workshop on Semantic Evaluation

In this paper we introduce our system for the task of Irony detection in English tweets, a part of SemEval 2018. We propose representation learning approach that relies on a multi-layered bidirectional LSTM, without using external features that provide additional semantic information. Although our model is able to outperform the baseline in the validation set, our results show limited generalization power over the test set. Given the limited size of the dataset, we think the usage of more pre-training schemes would greatly improve the obtained results.

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Deep contextualized word representations for detecting sarcasm and irony
Suzana Ilić | Edison Marrese-Taylor | Jorge Balazs | Yutaka Matsuo
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

Predicting context-dependent and non-literal utterances like sarcastic and ironic expressions still remains a challenging task in NLP, as it goes beyond linguistic patterns, encompassing common sense and shared knowledge as crucial components. To capture complex morpho-syntactic features that can usually serve as indicators for irony or sarcasm across dynamic contexts, we propose a model that uses character-level vector representations of words, based on ELMo. We test our model on 7 different datasets derived from 3 different data sources, providing state-of-the-art performance in 6 of them, and otherwise offering competitive results.