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

Many studies have proposed methods for optimizing the dialogue performance of
an entire pipeline task-oriented dialogue system by jointly training modules in
the system using reinforcement learning. However, these methods are limited in
that they can only be applied to modules implemented using trainable
neural-based methods. To solve this problem, we propose a method for optimizing
a pipeline system composed of modules implemented with arbitrary methods for
dialogue performance. With our method, neural-based components called
post-processing networks (PPNs) are installed inside such a system to
post-process the output of each module. All PPNs are updated to improve the
overall dialogue performance of the system by using reinforcement learning, not
necessitating each module to be differentiable. Through dialogue simulation and
human evaluation on the MultiWOZ dataset, we show that our method can improve
the dialogue performance of pipeline systems consisting of various modules.

本文提出了一种使用后处理网络，在不需要每个模块可微分的情况下进行强化学习，从而优化由任意方法实现的模块组成的对话系统的对话性能。通过多轮对话模拟和人类评估，证明了此方法可以提高由各种模块组成的管道系统的对话性能（使用 MultiWOZ 数据集）。

后处理网络：使用强化学习优化管线任务定向对话系统的方法

Post-processing Networks: Method for Optimizing Pipeline Task-oriented Dialogue Systems using Reinforcement Learning

This paper describes the USTC-NEL system to the speech translation task of
the IWSLT Evaluation 2018. The system is a conventional pipeline system which
contains 3 modules: speech recognition, post-processing and machine
translation. We train a group of hybrid-HMM models for our speech recognition,
and for machine translation we train transformer based neural machine
translation models with speech recognition output style text as input.
Experiments conducted on the IWSLT 2018 task indicate that, compared to
baseline system from KIT, our system achieved 14.9 BLEU improvement.

本论文讨论了 USTC-NEL 系统在 2018 年 IWSLT 评估的语音翻译任务中的应用，使用了传统的管道系统，包含三个模块：语音识别、后处理和机器翻译。我们训练了一组混合 - HMM 模型进行语音识别，并使用语音识别输出风格的文本作为输入来训练基于 transformer 的神经机器翻译模型。在 IWSLT 2018 任务上的实验表明，相较于 KIT 的基线系统，我们的系统实现了 14.9 的 BLEU 改善。