“Kelly is a Warm Person, Joseph is a Role Model”: Gender Biases in LLM-Generated Reference Letters

The tasks of humor understanding and generation are challenging and subjective even for humans, requiring commonsense and real-world knowledge to master. Puns, in particular, add the challenge of fusing that knowledge with the ability to interpret lexical-semantic ambiguity. In this paper, we present the ExPUNations (ExPUN) dataset, in which we augment an existing dataset of puns with detailed crowdsourced annotations of keywords denoting the most distinctive words that make the text funny, pun explanations describing why the text is funny, and fine-grained funniness ratings. This is the first humor dataset with such extensive and fine-grained annotations specifically for puns. Based on these annotations, we propose two tasks: explanation generation to aid with pun classification and keyword-conditioned pun generation, to challenge the current state-of-the-art natural language understanding and generation models' ability to understand and generate humor. We showcase that the annotated keywords we collect are helpful for generating better novel humorous texts in human evaluation, and that our natural language explanations can be leveraged to improve both the accuracy and robustness of humor classifiers.

ExPUNations: Augmenting Puns with Keywords and Explanations

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 a new dataset, 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 pun 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.

Context-Situated Pun Generation

The goal of the crime forecasting problem is to predict different types of crimes for each geographical region (like a neighborhood or censor tract) in the near future. Since nearby regions usually have similar socioeconomic characteristics which indicate similar crime patterns, recent state-of-the-art solutions constructed a distance-based region graph and utilized Graph Neural Network (GNN) techniques for crime forecasting, because the GNN techniques could effectively exploit the latent relationships between neighboring region nodes in the graph if the edges reveal high dependency or correlation. However, this distance-based pre-defined graph can not fully capture crime correlation between regions that are far from each other but share similar crime patterns. Hence, to make a more accurate crime prediction, the main challenge is to learn a better graph that reveals the dependencies between regions in crime occurrences and meanwhile captures the temporal patterns from historical crime records. To address these challenges, we propose an end-to-end graph convolutional recurrent network called HAGEN with several novel designs for crime prediction. Specifically, our framework could jointly capture the crime correlation between regions and the temporal crime dynamics by combining an adaptive region graph learning module with the Diffusion Convolution Gated Recurrent Unit (DCGRU). Based on the homophily assumption of GNN (i.e., graph convolution works better where neighboring nodes share the same label), we propose a homophily-aware constraint to regularize the optimization of the region graph so that neighboring region nodes on the learned graph share similar crime patterns, thus fitting the mechanism of diffusion convolution. Empirical experiments and comprehensive analysis on two real-world datasets showcase the effectiveness of HAGEN.

HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecasting

We propose to control paraphrase generation with carefully chosen target syntactic structures to generate more proper and higher-quality paraphrases. Our model, AESOP, leverages a pretrained language model and purposefully selected syntactical control via a retrieval-based selection module to generate fluent paraphrases. Experiments show that AESOP achieves state-of-the-art performances on semantic preservation and syntactic conformation on two benchmark datasets with ground-truth syntactic control from human-annotated exemplars. Moreover, with the retrieval-based target syntax selection module, AESOP generates paraphrases with even better qualities than the current best model using human-annotated target syntactic parses according to human evaluation. We further demonstrate the effectiveness of AESOP to improve classification models' robustness to syntactic perturbation by data augmentation on two GLUE tasks.

AESOP: Paraphrase Generation with Adaptive Syntactic Control

Stories and narratives are composed based on a variety of events. Understanding how these events are semantically related to each other is the essence of reading comprehension. Recent event-centric reading comprehension datasets focus on either event arguments or event temporal commonsense. Although these tasks evaluate machines' ability of narrative understanding, human-like reading comprehension requires the capability to process event-based semantics beyond arguments and temporal commonsense. For example, to understand causality between events, we need to infer motivations or purposes; to understand event hierarchy, we need to parse the composition of events. To facilitate these tasks, we introduce ESTER, a comprehensive machine reading comprehension (MRC) dataset for Event Semantic Relation Reasoning. We study the five most commonly used event semantic relations and formulate them as question answering tasks. Experimental results show that the current SOTA systems achieve 60.5%, 57.8%, and 76.3% for event-based F1, token-based F1, and HIT@1 scores respectively, which are significantly below human performances.

ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations

Human activities can be seen as sequences of events, which are crucial to understanding societies. Disproportional event distribution for different demographic groups can manifest and amplify social stereotypes, and potentially jeopardize the ability of members in some groups to pursue certain goals. In this paper, we present the first event-centric study of gender biases in a Wikipedia corpus. To facilitate the study, we curate a corpus of career and personal life descriptions with demographic information consisting of 7,854 fragments from 10,412 celebrities. Then we detect events with a state-of-the-art event detection model, calibrate the results using strategically generated templates, and extract events that have asymmetric associations with genders. Our study discovers that the Wikipedia pages tend to intermingle personal life events with professional events for females but not for males, which calls for the awareness of the Wikipedia community to formalize guidelines and train the editors to mind the implicit biases that contributors carry. Our work also lays the foundation for future works on quantifying and discovering event biases at the corpus level.

Men Are Elected, Women Are Married: Events Gender Bias on Wikipedia

We present EventPlus, a temporal event understanding pipeline that integrates various state-of-the-art event understanding components including event trigger and type detection, event argument detection, event duration and temporal relation extraction. Event information, especially event temporal knowledge, is a type of common sense knowledge that helps people understand how stories evolve and provides predictive hints for future events. EventPlus as the first comprehensive temporal event understanding pipeline provides a convenient tool for users to quickly obtain annotations about events and their temporal information for any user-provided document. Furthermore, we show EventPlus can be easily adapted to other domains (e.g., biomedical domain). We make EventPlus publicly available to facilitate event-related information extraction and downstream applications.


EventPlus: A Temporal Event Understanding Pipeline

*This is an IN-PERSON ONLY workshop. Session recording will be available here.* **Workshop Organizers:** Sebastian Gehrmann, Khyathi Raghavi Chandu, Alex Wang, Elizabeth Clark, Kaustubh Dhole and João Sedoc
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*Coffee Break 3:30 pm* [**Workshop Website**](https://gem-benchmark.com/workshop) Many new NLP applications are cast through the lens of natural language generation. With the advent of these new approaches, many opportunities arise: generation in previously less studied languages, new evaluation paradigms, methods for corpus creation, more efficient architectures, strategies for safe deployments, among many others. At the same time, we can learn from the rich history of NLG research to further improve generation methods. These developments require robust and sound NLG evaluation processes. To that end, the GEM workshop aims to encourage the development of model auditing and human evaluation strategies, and to popularize model evaluations in languages beyond English. [**Schedule**](https://gem-benchmark.com/workshop)
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workshop paper

**Welcome to EMNLP 2023!**

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Poster Session 18

poster

## Welcome to EMNLP 2022!
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***EMNLP 2022 General Chair***
 
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EMNLP 2022

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Theme Track

Poster Session 6

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technical paper

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AAAI 2022

Virtual Poster Session II: Generation

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7D: Resources and Evaluation 3

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4E: Ethics in NLP #1

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IJCNLP-AACL 2021

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10F-Oral: System Demonstrations

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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<figure class="video_container">
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Jiao Sun

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SHORT BIO

Presentations

“Kelly is a Warm Person, Joseph is a Role Model”: Gender Biases in LLM-Generated Reference Letters

ExPUNations: Augmenting Puns with Keywords and Explanations

Context-Situated Pun Generation

HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecasting

AESOP: Paraphrase Generation with Adaptive Syntactic Control

ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations

ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations

Men Are Elected, Women Are Married: Events Gender Bias on Wikipedia

EventPlus: A Temporal Event Understanding Pipeline

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PRESENTATIONS

CONFERENCES

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RESOURCES