Machine-Generated Text (MGT) detection, a task that discriminates MGT from
Human-Written Text (HWT), plays a crucial role in preventing misuse of text
generative models, which excel in mimicking human writing style recently.
Latest proposed detectors usually take coarse text sequence as input and output
some good results by fine-tune pretrained models with standard cross-entropy
loss. However, these methods fail to consider the linguistic aspect of text
(e.g., coherence) and sentence-level structures. Moreover, they lack the
ability to handle the low-resource problem which could often happen in practice
considering the enormous amount of textual data online. In this paper, we
present a coherence-based contrastive learning model named CoCo to detect the
possible MGT under low-resource scenario. Inspired by the distinctiveness and
permanence properties of linguistic feature, we represent text as a coherence
graph to capture its entity consistency, which is further encoded by the
pretrained model and graph neural network. To tackle the challenges of data
limitations, we employ a contrastive learning framework and propose an improved
contrastive loss for making full use of hard negative samples in training
stage. The experiment results on two public datasets prove our approach
outperforms the state-of-art methods significantly.

本论文提出了一种名为 CoCo 的基于连贯性图和对比学习的模型，用于检测低资源情况下的机器生成文本；该方法采用预训练模型和图神经网络将文本表示为连贯性图，并使用改进的对比损失函数应对数据限制挑战，实验结果表明 CoCo 方法优于现有的最先进的方法。

CoCo: 在数据限制下利用对比学习增强一致性的机器生成文本检测

CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Data Limitation With Contrastive Learning

Coherent discourse is distinguished from a mere collection of utterances by
the satisfaction of a diverse set of constraints, for example choice of
expression, logical relation between denoted events, and implicit compatibility
with world-knowledge. Do neural language models encode such constraints? We
design an extendable set of test suites addressing different aspects of
discourse and dialogue coherence. Unlike most previous coherence evaluation
studies, we address specific linguistic devices beyond sentence order
perturbations, allowing for a more fine-grained analysis of what constitutes
coherence and what neural models trained on a language modelling objective do
encode. Extending the targeted evaluation paradigm for neural language models
(Marvin and Linzen, 2018) to phenomena beyond syntax, we show that this
paradigm is equally suited to evaluate linguistic qualities that contribute to
the notion of coherence.

本文通过设计一系列测试集来评估神经语言模型是否编码了逻辑关系、内在一致性和世界知识这些与上下文有关的复杂语言结构，研究发现通过这样的测试集，可以更好的评估语言模型的质量。