Abusive language is a concerning problem in online social media. Past
research on detecting abusive language covers different platforms, languages,
demographies, etc. However, models trained using these datasets do not perform
well in cross-domain evaluation settings. To overcome this, a common strategy
is to use a few samples from the target domain to train models to get better
performance in that domain (cross-domain few-shot training). However, this
might cause the models to overfit the artefacts of those samples. A compelling
solution could be to guide the models toward rationales, i.e., spans of text
that justify the text's label. This method has been found to improve model
performance in the in-domain setting across various NLP tasks. In this paper,
we propose RAFT (Rationale Adaptor for Few-shoT classification) for abusive
language detection. We first build a multitask learning setup to jointly learn
rationales, targets, and labels, and find a significant improvement of 6% macro
F1 on the rationale detection task over training solely rationale classifiers.
We introduce two rationale-integrated BERT-based architectures (the RAFT
models) and evaluate our systems over five different abusive language datasets,
finding that in the few-shot classification setting, RAFT-based models
outperform baseline models by about 7% in macro F1 scores and perform
competitively to models finetuned on other source domains. Furthermore,
RAFT-based models outperform LIME/SHAP-based approaches in terms of
plausibility and are close in performance in terms of faithfulness.

研究旨在通过使用 RAFT (Rationale Adaptor for Few-shoT classification) 来检测网络社交媒体中的恶意语言。该方法使用多任务学习设置联合学习合理性、目标和标签，建立了两种合理性集成的 BERT-based 架构。在五个不同的虐待性语言数据集上，与基线模型相比，RAFT-based 模型在少量样本分类方面的性能提高了约 7％的宏 F1 分数，并在可信性方面胜过了 LIME / SHAP-based 方法。

RAFT：针对少样本滥用语言检测的理由适配器

RAFT: Rationale adaptor for few-shot abusive language detection

We present a new Convolutional Neural Network (CNN) model for text
classification that jointly exploits labels on documents and their component
sentences. Specifically, we consider scenarios in which annotators explicitly
mark sentences (or snippets) that support their overall document
categorization, i.e., they provide rationales. Our model exploits such
supervision via a hierarchical approach in which each document is represented
by a linear combination of the vector representations of its component
sentences. We propose a sentence-level convolutional model that estimates the
probability that a given sentence is a rationale, and we then scale the
contribution of each sentence to the aggregate document representation in
proportion to these estimates. Experiments on five classification datasets that
have document labels and associated rationales demonstrate that our approach
consistently outperforms strong baselines. Moreover, our model naturally
provides explanations for its predictions.

提出了一种新的卷积神经网络模型，可以在文本分类和理解上进行监督学习，以推断支持文件分类的原因，从而提升分类性能，并且能够自然地为分类结果提供解释。