A Report on the Euphemisms Detection Shared Task
- URL: http://arxiv.org/abs/2211.13327v1
- Date: Wed, 23 Nov 2022 22:06:35 GMT
- Title: A Report on the Euphemisms Detection Shared Task
- Authors: Patrick Lee and Anna Feldman and Jing Peng
- Abstract summary: This paper presents The Shared Task on Euphemism Detection for the Third Workshop on Figurative Language Processing (Fig 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.
- Score: 2.9972063833424216
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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
Related papers
- Learning from Emotions, Demographic Information and Implicit User
Feedback in Task-Oriented Document-Grounded Dialogues [59.516187851808375]
We introduce FEDI, the first English dialogue dataset for task-oriented document-grounded dialogues annotated with demographic information, user emotions and implicit feedback.
Our experiments with FLAN-T5, GPT-2 and LLaMA-2 show that these data have the potential to improve task completion and the factual consistency of the generated responses and user acceptance.
arXiv Detail & Related papers (2024-01-17T14:52:26Z) - Aspect-based Meeting Transcript Summarization: A Two-Stage Approach with
Weak Supervision on Sentence Classification [91.13086984529706]
Aspect-based meeting transcript summarization aims to produce multiple summaries.
Traditional summarization methods produce one summary mixing information of all aspects.
We propose a two-stage method for aspect-based meeting transcript summarization.
arXiv Detail & Related papers (2023-11-07T19:06:31Z) - Unify word-level and span-level tasks: NJUNLP's Participation for the
WMT2023 Quality Estimation Shared Task [59.46906545506715]
We introduce the NJUNLP team to the WMT 2023 Quality Estimation (QE) shared task.
Our team submitted predictions for the English-German language pair on all two sub-tasks.
Our models achieved the best results in English-German for both word-level and fine-grained error span detection sub-tasks.
arXiv Detail & Related papers (2023-09-23T01:52:14Z) - FEED PETs: Further Experimentation and Expansion on the Disambiguation
of Potentially Euphemistic Terms [3.1648534725322666]
We present novel euphemism corpora in three different languages: Yoruba, Spanish, and Mandarin Chinese.
We find that transformers are generally better at classifying vague PETs.
We perform euphemism disambiguation experiments in each language using multilingual transformer models mBERT and XLM-RoBERTa.
arXiv Detail & Related papers (2023-05-31T22:23:20Z) - SemEval-2023 Task 11: Learning With Disagreements (LeWiDi) [75.85548747729466]
We report on the second edition of the LeWiDi series of shared tasks.
This second edition attracted a wide array of participants resulting in 13 shared task submission papers.
arXiv Detail & Related papers (2023-04-28T12:20:35Z) - Detecting Euphemisms with Literal Descriptions and Visual Imagery [18.510509701709054]
This paper describes our two-stage system for the Euphemism Detection shared task hosted by the 3rd Workshop on Figurative Language Processing in conjunction with EMNLP 2022.
In the first stage, we seek to mitigate this ambiguity by incorporating literal descriptions into input text prompts to our baseline model. It turns out that this kind of direct supervision yields remarkable performance improvement.
In the second stage, we integrate visual supervision into our system using visual imageries, two sets of images generated by a text-to-image model by taking terms and descriptions as input. Our experiments demonstrate that visual supervision also gives a statistically significant performance boost.
arXiv Detail & Related papers (2022-11-08T21:50:05Z) - RuArg-2022: Argument Mining Evaluation [69.87149207721035]
This paper is a report of the organizers on the first competition of argumentation analysis systems dealing with Russian language texts.
A corpus containing 9,550 sentences (comments on social media posts) on three topics related to the COVID-19 pandemic was prepared.
The system that won the first place in both tasks used the NLI (Natural Language Inference) variant of the BERT architecture.
arXiv Detail & Related papers (2022-06-18T17:13:37Z) - Searching for PETs: Using Distributional and Sentiment-Based Methods to
Find Potentially Euphemistic Terms [2.666791490663749]
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.
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.
arXiv Detail & Related papers (2022-05-20T22:21:21Z) - CATs are Fuzzy PETs: A Corpus and Analysis of Potentially Euphemistic
Terms [2.666791490663749]
We present a corpus of potentially euphemistic terms (PETs) along with example texts from the GloWbE corpus.
We find that sentiment analysis on the euphemistic texts supports that PETs generally decrease negative and offensive sentiment.
We observe cases of disagreement in an annotation task, where humans are asked to label PETs as euphemistic or not.
arXiv Detail & Related papers (2022-05-05T16:01:39Z) - Potential Idiomatic Expression (PIE)-English: Corpus for Classes of
Idioms [1.6111818380407035]
This is the first dataset with classes of idioms beyond the literal and the general idioms classification.
This dataset contains over 20,100 samples with almost 1,200 cases of idioms (with their meanings) from 10 classes (or senses)
arXiv Detail & Related papers (2021-04-25T13:05:29Z) - RUSSE'2020: Findings of the First Taxonomy Enrichment Task for the
Russian language [70.27072729280528]
This paper describes the results of the first shared task on taxonomy enrichment for the Russian language.
16 teams participated in the task demonstrating high results with more than half of them outperforming the provided baseline.
arXiv Detail & Related papers (2020-05-22T13:30:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.