Given the dynamic nature of toxic language use, automated methods for
detecting toxic spans are likely to encounter distributional shift. To explore
this phenomenon, we evaluate three approaches for detecting toxic spans under
cross-domain conditions: lexicon-based, rationale extraction, and fine-tuned
language models. Our findings indicate that a simple method using off-the-shelf
lexicons performs best in the cross-domain setup. The cross-domain error
analysis suggests that (1) rationale extraction methods are prone to false
negatives, while (2) language models, despite performing best for the in-domain
case, recall fewer explicitly toxic words than lexicons and are prone to
certain types of false positives. Our code is publicly available at:
this https URL

本文探讨了检测有害文本段落时跨领域条件下的分布偏移现象，并评估了基于词汇库、原理提取和微调语言模型三种方法的效果，结果表明在跨领域条件下，采用通用的词汇库进行检测的方法效果最好。此外，该研究发现，(1) 基于原理提取的方法容易产生假阴性，(2) 对于本域情况，语言模型的表现最好，但是回溯的有害词汇比词汇库更少，且容易出现某些类型的假阳性，文章提出的代码公开可用。

跨领域有害文本片段检测

Cross-Domain Toxic Spans Detection

Recent works on two-stage cross-domain detection have widely explored the
local feature patterns to achieve more accurate adaptation results. These
methods heavily rely on the region proposal mechanisms and ROI-based
instance-level features to design fine-grained feature alignment modules with
respect to the foreground objects. However, for one-stage detectors, it is hard
or even impossible to obtain explicit instance-level features in the detection
pipelines. Motivated by this, we propose an Implicit Instance-Invariant Network
(I3Net), which is tailored for adapting one-stage detectors and implicitly
learns instance-invariant features via exploiting the natural characteristics
of deep features in different layers. Specifically, we facilitate the
adaptation from three aspects: (1) Dynamic and Class-Balanced Reweighting
(DCBR) strategy, which considers the coexistence of intra-domain and
intra-class variations to assign larger weights to those sample-scarce
categories and easy-to-adapt samples; (2) Category-aware Object Pattern
Matching (COPM) module, which boosts the cross-domain foreground objects
matching guided by the categorical information and suppresses the uninformative
background features; (3) Regularized Joint Category Alignment (RJCA) module,
which jointly enforces the category alignment at different domain-specific
layers with a consistency regularization. Experiments reveal that I3Net exceeds
the state-of-the-art performance on benchmark datasets.

本文提出了一种适用于单级检测器的隐式实例不变网络（I3Net），通过利用不同层次深度特征的自然特点，从三个方面促进适应：动态和类平衡权重（DCBR）策略、类别感知对象模式匹配（COPM）模块和正则化联合类别对齐（RJCA）模块。实验结果表明，I3Net 在基准数据集上优于现有技术的性能。

I3Net：隐式实例不变网络用于调整一阶段物体探测器

I3Net: Implicit Instance-Invariant Network for Adapting One-Stage Object  Detectors

Applying the knowledge of an object detector trained on a specific domain
directly onto a new domain is risky, as the gap between two domains can
severely degrade model's performance. Furthermore, since different instances
commonly embody distinct modal information in object detection scenario, the
feature alignment of source and target domain is hard to be realized. To
mitigate these problems, we propose a Graph-induced Prototype Alignment (GPA)
framework to seek for category-level domain alignment via elaborate prototype
representations. In the nutshell, more precise instance-level features are
obtained through graph-based information propagation among region proposals,
and, on such basis, the prototype representation of each class is derived for
category-level domain alignment. In addition, in order to alleviate the
negative effect of class-imbalance on domain adaptation, we design a
Class-reweighted Contrastive Loss to harmonize the adaptation training process.
Combining with Faster R-CNN, the proposed framework conducts feature alignment
in a two-stage manner. Comprehensive results on various cross-domain detection
tasks demonstrate that our approach outperforms existing methods with a
remarkable margin. Our code is available at
this https URL

通过图形感知原型对齐（GPA）框架和加权对比损失最小化类不平衡，结合 Faster R-CNN 的特征对齐方式，可实现目标探测跨域适应。