The forum r/AmITheAsshole in Reddit hosts discussion on moral issues based on
concrete narratives presented by users. Existing analysis of the forum focuses
on its comments, and does not make the underlying data publicly available. In
this paper we build a new dataset of comments and also investigate the
classification of the posts in the forum. Further, we identify textual patterns
associated with the provocation of moral judgement by posts, with the
expression of moral stance in comments, and with the decisions of trained
classifiers of posts and comments.

本文研究 Reddit 上面的 r/AmITheAsshole 论坛，通过建立新数据集并对评论进行分析，发现了帖子所使用的文本模式与道德判断，评论中的道德立场表达，以及训练有素的分类器对帖子和评论进行决策的关联性。

Reddit 上道德判断的区分和预测的可解释模式

Explainable Patterns for Distinction and Prediction of Moral Judgement on Reddit

Combining a pretrained language model (PLM) with textual patterns has been
shown to help in both zero- and few-shot settings. For zero-shot performance,
it makes sense to design patterns that closely resemble the text seen during
self-supervised pretraining because the model has never seen anything else.
Supervised training allows for more flexibility. If we allow for tokens outside
the PLM's vocabulary, patterns can be adapted more flexibly to a PLM's
idiosyncrasies. Contrasting patterns where a "token" can be any continuous
vector vs. those where a discrete choice between vocabulary elements has to be
made, we call our method CONtinuous pAtterNs (CONAN). We evaluate CONAN on two
established benchmarks for lexical inference in context (LIiC) a.k.a. predicate
entailment, a challenging natural language understanding task with relatively
small training sets. In a direct comparison with discrete patterns, CONAN
consistently leads to improved performance, setting a new state of the art. Our
experiments give valuable insights into the kind of pattern that enhances a
PLM's performance on LIiC and raise important questions regarding our
understanding of PLMs using text patterns.

采用预训练语言模型与文本模式的结合，可提升 PLM 在文本识别中的性能，其中连续型文本模式的采用可进一步提高 PLM 在 LIiC 中的性能并带来有用的启示。

上下文中的词汇推理连续蕴含模式

Continuous Entailment Patterns for Lexical Inference in Context

Shortage of available training data is holding back progress in the area of
automated error detection. This paper investigates two alternative methods for
artificially generating writing errors, in order to create additional
resources. We propose treating error generation as a machine translation task,
where grammatically correct text is translated to contain errors. In addition,
we explore a system for extracting textual patterns from an annotated corpus,
which can then be used to insert errors into grammatically correct sentences.
Our experiments show that the inclusion of artificially generated errors
significantly improves error detection accuracy on both FCE and CoNLL 2014
datasets.

本研究探讨了两种替代方法：机器翻译和文本模式提取以解决短缺的数据对自动化错误检测的影响。实验表明：人工生成的错误明显提高了对 FCE 和 CoNLL 2014 数据集的错误检测精度。

利用机器翻译和句法模式进行人造错误生成

Artificial Error Generation with Machine Translation and Syntactic  Patterns

Recognizing entity synonyms from text has become a crucial task in many
entity-leveraging applications. However, discovering entity synonyms from
domain-specific text corpora (e.g., news articles, scientific papers) is rather
challenging. Current systems take an entity name string as input to find out
other names that are synonymous, ignoring the fact that often times a name
string can refer to multiple entities (e.g., "apple" could refer to both Apple
Inc and the fruit apple). Moreover, most existing methods require training data
manually created by domain experts to construct supervised-learning systems. In
this paper, we study the problem of automatic synonym discovery with knowledge
bases, that is, identifying synonyms for knowledge base entities in a given
domain-specific corpus. The manually-curated synonyms for each entity stored in
a knowledge base not only form a set of name strings to disambiguate the
meaning for each other, but also can serve as "distant" supervision to help
determine important features for the task. We propose a novel framework, called
DPE, to integrate two kinds of mutually-complementing signals for synonym
discovery, i.e., distributional features based on corpus-level statistics and
textual patterns based on local contexts. In particular, DPE jointly optimizes
the two kinds of signals in conjunction with distant supervision, so that they
can mutually enhance each other in the training stage. At the inference stage,
both signals will be utilized to discover synonyms for the given entities.
Experimental results prove the effectiveness of the proposed framework.

提出了一个新的框架 DPE，通过结合远程监督和语料库级别统计的分布特征和局部上下文的文本模式来相互补充信号，从而发现特定领域知识库实体的同义词。