Papers by Manuel Montes
Early Text Classification Using Multi-Resolution Concept Representations (N18-1)
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| Challenge: | e-communications have been misused by cyber-criminals, who hide in the depths of the web. |
| Approach: | They propose a document representation which allows us to generate multiple "views" of the analyzed text. |
| Outcome: | The proposed representation outperforms existing models in two tasks where anticipation is critical: sexual predator detection and depression detection. |
DisorBERT: A Double Domain Adaptation Model for Detecting Signs of Mental Disorders in Social Media (2023.acl-long)
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| Challenge: | Mental disorders affect millions of people worldwide and cause interference with their thinking and behavior. |
| Approach: | They propose to adapt a social media-based mental health model to automatically analyze social media content to detect signs of mental disorders. |
| Outcome: | The proposed model improves classification performance and competitiveness against state-of-the-art methods. |
A Genre-Aware Attention Model to Improve the Likability Prediction of Books (D18-1)
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| Challenge: | Existing methods for likability prediction are time-consuming and too rigid. |
| Approach: | They propose a novel neural architecture that incorporates genre supervision to assign weights to individual feature types based on the characteristics of each book. |
| Outcome: | The proposed method outperforms state-of-the-art methods and achieves competitive results. |
GAttention: Gated Attention for the Detection of Abusive Language (2025.findings-emnlp)
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| Challenge: | Abusive language online creates toxic environments and exacerbates social tensions, underscoring the need for robust NLP models to interpret nuanced linguistic cues. |
| Approach: | They propose a Gated Attention mechanism that combines the strengths of Contextual attention and Self-attention mechanisms to address the limitations of existing attention models within the text classification task. |
| Outcome: | The novel gated attention mechanism addresses the limitations of existing attention models within the text classification task. |
Letting Emotions Flow: Success Prediction by Modeling the Flow of Emotions in Books (N18-2)
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| Challenge: | We obtained the best weighted F1-score of 69% for predicting books’ success in a multitask setting. |
| Approach: | They propose to model the flow of emotions over a book using recurrent neural networks and quantify its usefulness in predicting success in books. |
| Outcome: | The proposed model obtained the best weighted F1-score of 69% for predicting books’ success in a multitask setting. |