DeepBlues@LT-EDI-ACL2022: Depression level detection modelling through domain specific BERT and short text Depression classifiers
Nawshad Farruque | Osmar Zaiane | Randy Goebel | Sudhakar Sivapalan
Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion
We discuss a variety of approaches to build a robust Depression level detection model from longer social media posts (i.e., Reddit Depression forum posts) using a mental health text pre-trained BERT model. Further, we report our experimental results based on a strategy to select excerpts from long text and then fine-tune the BERT model to combat the issue of memory constraints while processing such texts. We show that, with domain specific BERT, we can achieve reasonable accuracy with fixed text size (in this case 200 tokens) for this task. In addition we can use short text classifiers to extract relevant text from the long text and achieve slightly better accuracy, albeit, trading off with the processing time for extracting such excerpts.
Seq2Emo: A Sequence to Multi-Label Emotion Classification Model
Chenyang Huang | Amine Trabelsi | Xuebin Qin | Nawshad Farruque | Lili Mou | Osmar Zaïane
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Multi-label emotion classification is an important task in NLP and is essential to many applications. In this work, we propose a sequence-to-emotion (Seq2Emo) approach, which implicitly models emotion correlations in a bi-directional decoder. Experiments on SemEval’18 and GoEmotions datasets show that our approach outperforms state-of-the-art methods (without using external data). In particular, Seq2Emo outperforms the binary relevance (BR) and classifier chain (CC) approaches in a fair setting.
- Osmar R. Zaiane 2
- Chenyang Huang 1
- Amine Trabelsi 1
- Xuebin Qin 1
- Lili Mou 1
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