We propose a unified framework to generate both homophonic and homographic
puns to resolve the split-up in existing works. Specifically, we incorporate
three linguistic attributes of puns to the language models: ambiguity,
distinctiveness, and surprise. Our framework consists of three parts: 1) a
context words/phrases selector to promote the aforementioned attributes, 2) a
generation model trained on non-pun sentences to incorporate the context
words/phrases into the generation output, and 3) a label predictor that learns
the structure of puns which is used to steer the generation model at inference
time. Evaluation results on both pun types demonstrate the efficacy of our
model over strong baselines.

提出了一个统一框架，用于生成同音和同形双关语以解决语言模型中的分裂问题。该框架包括三个部分：1）上下文单词 / 短语选择器，以促进双关语的歧义性、独特性和惊喜性；2）一个针对非双关语句子进行训练的生成模型，将上下文单词 / 短语纳入生成结果；3）一个标签预测器，学习了双关语的结构，用于引导推理时的生成模型。对双关语类型的评估结果表明，该模型相对于基线模型具有更好的效果。

基于幽默原则的双关语生成统一框架

A Unified Framework for Pun Generation with Humor Principles

Several machine learning-based spoiler detection models have been proposed
recently to protect users from spoilers on review websites. Although dependency
relations between context words are important for detecting spoilers, current
attention-based spoiler detection models are insufficient for utilizing
dependency relations. To address this problem, we propose a new spoiler
detection model called SDGNN that is based on syntax-aware graph neural
networks. In the experiments on two real-world benchmark datasets, we show that
our SDGNN outperforms the existing spoiler detection models.

提出了一种名为 SDGNN 的新型剧透检测模型，基于语法感知的图神经网络，通过对依赖关系的利用达到更好的效果，实验证明其在两种真实的基准数据集上表现优于现有的剧透检测模型。

基于图神经网络和依赖关系感知注意机制的剧透检测模型

"Killing Me" Is Not a Spoiler: Spoiler Detection Model using Graph  Neural Networks with Dependency Relation-Aware Attention Mechanism

Target-dependent sentiment classification remains a challenge: modeling the
semantic relatedness of a target with its context words in a sentence.
Different context words have different influences on determining the sentiment
polarity of a sentence towards the target. Therefore, it is desirable to
integrate the connections between target word and context words when building a
learning system. In this paper, we develop two target dependent long short-term
memory (LSTM) models, where target information is automatically taken into
account. We evaluate our methods on a benchmark dataset from Twitter. Empirical
results show that modeling sentence representation with standard LSTM does not
perform well. Incorporating target information into LSTM can significantly
boost the classification accuracy. The target-dependent LSTM models achieve
state-of-the-art performances without using syntactic parser or external
sentiment lexicons.

本研究提出了两种基于 LSTM 的目标相关情感分类模型，通过将目标词与上下文词的语义相关性进行建模，相比传统的 LSTM 模型，这种模型可以显著提高分类准确度，并取得了最新的最优性能。