Leveraging Existing Datasets to Efficiently Detect Abusive Content with Few-Shot Learning

Core Concepts

By leveraging a wide range of existing datasets related to abusive language detection, a two-step approach can efficiently build models for new target tasks using only a few annotated samples.

Abstract

The paper proposes a multi-dataset training (MDT) approach to efficiently build models for new abusive content detection tasks using only a few annotated samples from the target dataset.
The key insights are:

Leveraging a wide range of existing datasets related to abusive language detection, including hate speech, offensive language, abuse, sexism, and racism detection, can help the model learn a general understanding of abusive language.

In the first step, the model is trained in a multi-task fashion on the external datasets to acquire this general abusive language awareness.

In the second step, the model is adapted to the specific requirements of the target task using only a few annotated samples (4-shots in the main experiments).

The experiments show that this two-step approach outperforms various baselines, including monolingual and cross-lingual settings, on both binary and fine-grained test sets. The model even improves on unseen target labels, demonstrating its ability to acquire a general understanding of abusive language.
The analysis further reveals that labels not directly used in the target task also contribute to the model's performance, highlighting the benefits of the diverse external dataset coverage. The authors also conduct diagnostics using the HateCheck test suite, supporting the claim of better general abusive language understanding.

Stats

"Already a large set of annotated corpora with different properties and label sets were created, such as hate or misogyny detection, but the form and targets of abusive speech are constantly evolving."
"To reduce annotation costs, related work leveraged transfer learning to build systems across languages and domains."
"Our experiments show improved performance when training using the external datasets compared to various baselines, including both monolingual and cross-lingual settings, on both binary and fine-grained test sets."
"We find that even unseen target labels are improved due to the better general abusive language understanding of our models."

Quotes

"To push back abusive online content, various automated systems, and more importantly datasets (Poletto et al., 2021), were introduced covering various text genres such as forum (de Gibert et al., 2018), Twitter (Struß et al., 2019) or Instagram posts (Suryawanshi et al., 2020) of various languages (Vidgen and Derczynski, 2020), user groups such as women (Fersini et al., 2018) or LGBTQ+ (Leite et al., 2020) and tasks including hate speech (de Gibert et al., 2018), offensive language (Zampieri et al., 2019) or toxicity (Leite et al., 2020) detection, etc."
"Our experiments show improved performance when training using the external datasets compared to various baselines, including both monolingual and cross-lingual settings, on both binary and fine-grained test sets."
"We find that even unseen target labels are improved due to the better general abusive language understanding of our models."

Key Insights Distilled From

How to Solve Few-Shot Abusive Content Detection Using the Data We Actually Have

by Viktor Hangy... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2305.14081.pdf

Deeper Inquiries

How can the proposed approach be extended to incorporate domain-specific knowledge beyond the general abusive language understanding

To incorporate domain-specific knowledge beyond general abusive language understanding, the proposed approach can be extended by introducing a mechanism for fine-tuning the model with domain-specific data. This can be achieved by adding an additional step after the few-shot adaptation to the target requirements. In this step, the model can be further trained on a small set of domain-specific examples to refine its understanding of the nuances and intricacies of the particular domain. By incorporating domain-specific data, the model can learn to identify context-specific patterns and language usage that are unique to the target domain, thereby enhancing its performance in detecting abusive content within that specific context.

What are the potential limitations of the two-step training approach, and how could it be further improved to handle more complex target task requirements

The two-step training approach, while effective, may have limitations when handling more complex target task requirements. One potential limitation is the scalability of the approach to handle a large number of target labels or a highly diverse set of target tasks. In such cases, the model may struggle to generalize effectively across a wide range of labels and tasks with limited training data. To address this limitation, the approach could be further improved by incorporating techniques such as active learning, where the model actively selects the most informative samples for annotation to maximize learning efficiency. Additionally, leveraging semi-supervised learning methods to utilize unlabeled data in conjunction with the few-shot annotated data can help improve model performance on complex target tasks.

Given the rapid evolution of abusive language, how could the model be continuously updated to maintain its effectiveness over time without requiring extensive retraining

To ensure the model's effectiveness over time and adapt to the evolving nature of abusive language, a continuous learning framework can be implemented. This framework would involve periodically retraining the model on new data to incorporate the latest trends and patterns in abusive language. One approach could be to set up a feedback loop where the model is regularly updated with new annotated data from ongoing monitoring of online content. Additionally, techniques like transfer learning can be employed to leverage knowledge from previously trained models and adapt it to new data without requiring extensive retraining. By continuously updating the model with fresh data and incorporating advancements in the field of abusive language detection, the model can maintain its effectiveness and relevance over time.