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.

A Unified Framework for Pun Generation with Humor Principles

Poetry generation, and creative language generation in general, usually suffers from the lack of large training data. 
In this paper, we present a novel framework to generate sonnets that does not require training on poems. We design a hierarchical framework which plans the poem sketch before decoding. Specifically, a content planning module is trained on non-poetic texts to obtain discourse-level coherence; then a rhyme module generates rhyme words and a polishing module introduces imagery and similes for aesthetics purposes. Finally, we design a constrained decoding algorithm to impose the meter-and-rhyme constraint of the generated sonnets. Automatic and human evaluation show that our multi-stage approach without training on poem corpora generates more coherent, poetic, and creative sonnets than several strong baselines.

Zero-shot Sonnet Generation with Discourse-level Planning and Aesthetics Features

In this paper, we propose a simple yet effective way to generate pun sentences that does not require any training on existing puns. Our approach is inspired by humor theories that ambiguity comes from the context rather than the pun word itself. Given a pair of definitions of a pun word, our model first produces a list of related concepts through a reverse dictionary. We then utilize one-shot GPT3 to generate context words and then generate puns incorporating context words from both concepts. Human evaluation shows that our method successfully generates pun 52\% of the time, outperforming well-crafted baselines and the state-of-the-art models by a large margin.

AmbiPun: Generating Humorous Puns with Ambiguous Context

A hyperbole is an intentional and creative exaggeration not to be taken literally. Despite its ubiquity in daily life, the computational explorations of hyperboles are scarce. In this paper, we tackle the under-explored and challenging task: sentence-level hyperbole generation. 
We start with a representative syntactic pattern for intensification and systematically study the semantic (commonsense and counterfactual) relationships between each component in such hyperboles. We then leverage commonsense and counterfactual inference to generate hyperbole candidates based on our findings from the pattern, and train neural classifiers to rank and select high-quality hyperboles. 
Automatic and human evaluations show that our generation method is able to generate hyperboles with high success rate, intensity, funniness, and creativity.

HypoGen: Hyperbole Generation with Commonsense and Counterfactual Knowledge

**Chair:** Debanjan Ghosh (remote) **Organizers:** Debanjan Ghosh, Beata Beigman Klebanov, Smaranda Muresan, Anna Feldman, Soujanya Poria and Tuhin Chakrabarty Please visit our [**website**](https://sites.google.com/view/figlang2022) for more information and detailed schedule.

**RocketChat channel for this workshop:** https://emnlp2022.rocket.chat/channel/workshop-17-flp

W17 (FLP) 3rd Workshop on Figurative Language Processing

workshop paper

## Welcome to EMNLP 2022!
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9A: Language Generation

technical paper

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
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NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

PS2-3: Language Generation

poster

Findings Papers - Generation

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

Yufei Tian

5

SHORT BIO

Presentations

A Unified Framework for Pun Generation with Humor Principles

Zero-shot Sonnet Generation with Discourse-level Planning and Aesthetics Features

AmbiPun: Generating Humorous Puns with Ambiguous Context

HypoGen: Hyperbole Generation with Commonsense and Counterfactual Knowledge

A Unified Framework for Pun Generation with Humor Principles

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