Previous work on pun generation commonly begins with a given pun word (a pair
of homophones for heterographic pun generation and a polyseme for homographic
pun generation) and seeks to generate an appropriate pun. While this may enable
efficient pun generation, we believe that a pun is most entertaining if it fits
appropriately within a given context, e.g., a given situation or dialogue. In
this work, we propose a new task, context-situated pun generation, where a
specific context represented by a set of keywords is provided, and the task is
to first identify suitable pun words that are appropriate for the context, then
generate puns based on the context keywords and the identified pun words. We
collect CUP (Context-sitUated Pun), containing 4.5k tuples of context words and
pun pairs. Based on the new data and setup, we propose a pipeline system for
context-situated pun generation, including a pun word retrieval module that
identifies suitable pun words for a given context, and a generation module that
generates puns from context keywords and pun words. Human evaluation shows that
69% of our top retrieved pun words can be used to generate context-situated
puns, and our generation module yields successful puns 31% of the time given a
plausible tuple of context words and pun pair, almost tripling the yield of a
state-of-the-art pun generation model. With an end-to-end evaluation, our
pipeline system with the top-1 retrieved pun pair for a given context can
generate successful puns 40% of the time, better than all other modeling
variations but 32% lower than the human success rate. This highlights the
difficulty of the task, and encourages more research in this direction.

本研究提出了一种上下文适应的谐音生成任务，并设计了一个基于谐音检索和生成的系统，能够通过给定的上下文关键词生成成功率为 40％的谐音，较先前的方法产生率高 32％。

情境下的双关语生成

Context-Situated Pun Generation

We tackle the problem of generating a pun sentence given a pair of homophones
(e.g., "died" and "dyed"). Supervised text generation is inappropriate due to
the lack of a large corpus of puns, and even if such a corpus existed, mimicry
is at odds with generating novel content. In this paper, we propose an
unsupervised approach to pun generation using a corpus of unhumorous text and
what we call the local-global surprisal principle: we posit that in a pun
sentence, there is a strong association between the pun word (e.g., "dyed") and
the distant context, as well as a strong association between the alternative
word (e.g., "died") and the immediate context. This contrast creates surprise
and thus humor. We instantiate this principle for pun generation in two ways:
(i) as a measure based on the ratio of probabilities under a language model,
and (ii) a retrieve-and-edit approach based on words suggested by a skip-gram
model. Human evaluation shows that our retrieve-and-edit approach generates
puns successfully 31% of the time, tripling the success rate of a neural
generation baseline.

本文提出了基于语言模型和局部 - 全局惊奇原则的双关语生成的无监督方法 —— 提取语料库中的非幽默文本进行生成，成功率较神经网络生成基线高三倍以上。