Neural sequence generation models are known to "hallucinate", by producing
outputs that are unrelated to the source text. These hallucinations are
potentially harmful, yet it remains unclear in what conditions they arise and
how to mitigate their impact. In this work, we first identify internal model
symptoms of hallucinations by analyzing the relative token contributions to the
generation in contrastive hallucinated vs. non-hallucinated outputs generated
via source perturbations. We then show that these symptoms are reliable
indicators of natural hallucinations, by using them to design a lightweight
hallucination detector which outperforms both model-free baselines and strong
classifiers based on quality estimation or large pre-trained models on manually
annotated English-Chinese and German-English translation test beds.

通过分析相对标记的贡献来确定神经序列生成模型的幻觉内在模型症状，使用这些症状设计出一个轻量级的幻觉检测器，在手动标注的英中和德英翻译测试上优于基于质量估计或大型预训练模型的分类器。

通过模型内省理解和检测神经机器翻译中的幻觉

Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspection

Redundancy-aware extractive summarization systems score the redundancy of the
sentences to be included in a summary either jointly with their salience
information or separately as an additional sentence scoring step. Previous work
shows the efficacy of jointly scoring and selecting sentences with neural
sequence generation models. It is, however, not well-understood if the gain is
due to better encoding techniques or better redundancy reduction approaches.
Similarly, the contribution of salience versus diversity components on the
created summary is not studied well. Building on the state-of-the-art encoding
methods for summarization, we present two adaptive learning models: AREDSUM-SEQ
that jointly considers salience and novelty during sentence selection; and a
two-step AREDSUM-CTX that scores salience first, then learns to balance
salience and redundancy, enabling the measurement of the impact of each aspect.
Empirical results on CNN/DailyMail and NYT50 datasets show that by modeling
diversity explicitly in a separate step, AREDSUM-CTX achieves significantly
better performance than AREDSUM-SEQ as well as state-of-the-art extractive
summarization baselines.

该研究提出了两个自适应模型，分别为 AREDSUM-SEQ 和 AREDSUM-CTX，分别考虑文本中的句子显著性、新颖性和多样性等特征，通过实验验证，AREDSUM-CTX 在 CNN/DailyMail 和 NYT50 数据集上表现优于 AREDSUM-SEQ 和抽取式摘要基线模型。

AREDSUM: 面向文档摘要的自适应冗余感知迭代句子排名

AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for  Extractive Document Summarization

While neural sequence generation models achieve initial success for many NLP
applications, the canonical decoding procedure with left-to-right generation
order (i.e., autoregressive) in one-pass can not reflect the true nature of
human revising a sentence to obtain a refined result. In this work, we propose
XL-Editor, a novel training framework that enables state-of-the-art generalized
autoregressive pretraining methods, XLNet specifically, to revise a given
sentence by the variable-length insertion probability. Concretely, XL-Editor
can (1) estimate the probability of inserting a variable-length sequence into a
specific position of a given sentence; (2) execute post-editing operations such
as insertion, deletion, and replacement based on the estimated variable-length
insertion probability; (3) complement existing sequence-to-sequence models to
refine the generated sequences. Empirically, we first demonstrate better
post-editing capabilities of XL-Editor over XLNet on the text insertion and
deletion tasks, which validates the effectiveness of our proposed framework.
Furthermore, we extend XL-Editor to the unpaired text style transfer task,
where transferring the target style onto a given sentence can be naturally
viewed as post-editing the sentence into the target style. XL-Editor achieves
significant improvement in style transfer accuracy and also maintains coherent
semantic of the original sentence, showing the broad applicability of our
method.

本文提出了一种名为 XL-Editor 的框架，通过可变长度插入概率使得 XLNet 模型可以修改给定的句子并进行后处理操作，无需按照自左向右的生成顺序；基于此，本文首先在文本插入和删除任务上展示了 XL-Editor 的改进效果，随后在非成对文本风格转换任务中实现了重大进展。