skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: FEED PETs: Further Experimentation and Expansion on the Disambiguation of Potentially Euphemistic Terms.
Transformers have been shown to work well for the task of English euphemism disambiguation, in which a potentially euphemistic term (PET) is classified as euphemistic or non-euphemistic in a particular context. In this study, we expand on the task in two ways. First, we annotate PETs for vagueness, a linguistic property associated with euphemisms, and find that transformers are generally better at classifying vague PETs, suggesting linguistic differences in the data that impact performance. Second, we present novel euphemism corpora in three different languages: Yoruba, Spanish, and Mandarin Chinese. We perform euphemism disambiguation experiments in each language using multilingual transformer models mBERT and XLM-RoBERTa, establishing preliminary results from which to launch future work.  more » « less
Award ID(s):
1704113
PAR ID:
10470678
Author(s) / Creator(s):
Publisher / Repository:
12th Joint Conference on Lexical and Computational Semantics (SEM 2023)
Date Published:
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; (, arXiv preprint arXiv:2205.02728.)
    Euphemisms have not received much attention in natural language processing, despite being an important element of polite and figurative language. Euphemisms prove to be a difficult topic, not only because they are subject to language change, but also because humans may not agree on what is a euphemism and what is not. Nevertheless, the first step to tackling the issue is to collect and analyze examples of euphemisms. We present a corpus of potentially euphemistic terms (PETs) along with example texts from the GloWbE corpus. Additionally, we present a subcorpus of texts where these PETs are not being used euphemistically, which may be useful for future applications. We also discuss the results of multiple analyses run on the corpus. Firstly, we find that sentiment analysis on the euphemistic texts supports that PETs generally decrease negative and offensive sentiment. Secondly, we observe cases of disagreement in an annotation task, where humans are asked to label PETs as euphemistic or not in a subset of our corpus text examples. We attribute the disagreement to a variety of potential reasons, including if the PET was a commonly accepted term (CAT). 
    more » « less
  2. (, UnImplicit: The Second Workshop on Understanding Implicit and Underspecified Language, co-located with NAACL 2022, Seattle, USA)
    This paper presents a linguistically driven proof of concept for finding potentially euphemistic terms, or PETs. Acknowledging that PETs tend to be commonly used expressions for a certain range of sensitive topics, we make use of distributional similarities to select and filter phrase candidates from a sentence and rank them using a set of simple sentimentbased metrics. We present the results of our approach tested on a corpus of sentences containing euphemisms, demonstrating its efficacy for detecting single and multi-word PETs from a broad range of topics. We also discuss future potential for sentiment-based methods on this task. 
    more » « less
  3. (, Third Workshop on Processing Figurative Language co-located with EMNLP 2022, Abu Dhabi, UAE)
    This paper presents The Shared Task on Euphemism Detection for the Third Workshop on Figurative Language Processing (FigLang 2022) held in conjunction with EMNLP 2022. Participants were invited to investigate the euphemism detection task: given input text, identify whether it contains a euphemism. The input data is a corpus of sentences containing potentially euphemistic terms (PETs) collected from the GloWbE corpus (Davies and Fuchs, 2015), and are human-annotated as containing either a euphemistic or literal usage of a PET. In this paper, we present the results and analyze the common themes, methods and findings of the participating teams. 
    more » « less
  4. ; ; ; (, Proceedings of the 16th International Conference of the Learning Sciences - ICLS 2022)
    Chinn, C.; Tan, E.; Chan, C. & (Ed.)
    Pets are beloved family members in many cultures. Companionship with pets motivates and positions humans as inquirers as they find out their pets' experiences with them. With the need to advance science education from dualist notions of the world and the learner as separate entities, our research team conducted a two-week online summer camp to engage teens and their pets in investigations around pets' senses. Following a qualitative analysis of participants' talk and projects at the workshop, we found that teens engaged in science learning practices while investigating aspects of their pets' lives and designing experiences for them. Additionally, participants adopted an ecological and relational approach to science learning that positioned themselves and their pets as subjects. We discuss implications for future work with pets, and for the design of other STEM learning environments that engage perspective-taking, empathy, and care. 
    more » « less
  5. ; ; ; (, Proceedings on Privacy Enhancing Technologies)
    Describing Privacy Enhancing Technologies (PETs) to the general public is challenging but essential to convey the privacy protections they provide. Existing research has explored the explanation of differential privacy in health contexts. Our study adapts well-performing textual descriptions of local differential privacy from prior work to a new context and broadens the investigation to the descriptions of additional PETs. Specifically, we develop user-centric textual descriptions for popular PETs in ad tracking and analytics, including local differential privacy, federated learning with and without local differential privacy, and Google's Topics. We examine the applicability of previous findings to these expanded contexts, and evaluate the PET descriptions with quantitative and qualitative survey data (n=306). We find that adapting a process- and implications-focused approach to the ad tracking and analytics context achieved similar effects in facilitating user understanding compared to health contexts, and that our descriptions developed with this process+implications approach for the additional, understudied PETs help users understand PETs' processes. We also find that incorporating an implications statement into PET descriptions did not hurt user comprehension but also did not achieve a significant positive effect, which contrasts prior findings in health contexts. We note that the use of technical terms as well as the machine learning aspect of PETs, even without delving into specifics, led to confusion for some respondents. Based on our findings, we offer recommendations and insights for crafting effective user-centric descriptions of privacy-enhancing technologies. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email