PejorativITy: Disambiguating Pejorative Epithets to Improve Misogyny Detection in Italian Tweets

• URL: http://arxiv.org/abs/2404.02681v1
• Date: Wed, 3 Apr 2024 12:24:48 GMT
• Title: PejorativITy: Disambiguating Pejorative Epithets to Improve Misogyny Detection in Italian Tweets
• Authors: Arianna Muti, Federico Ruggeri, Cagri Toraman, Lorenzo Musetti, Samuel Algherini, Silvia Ronchi, Gianmarco Saretto, Caterina Zapparoli, Alberto Barrón-Cedeño,
• Abstract summary: We present PejorativITy, a novel corpus of 1,200 manually annotated Italian tweets for language at the word level and misogyny at the sentence level. We evaluate the impact of injecting information about disambiguated words into a model targeting misogyny detection.
• Score: 11.224028161937296
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Misogyny is often expressed through figurative language. Some neutral words can assume a negative connotation when functioning as pejorative epithets. Disambiguating the meaning of such terms might help the detection of misogyny. In order to address such task, we present PejorativITy, a novel corpus of 1,200 manually annotated Italian tweets for pejorative language at the word level and misogyny at the sentence level. We evaluate the impact of injecting information about disambiguated words into a model targeting misogyny detection. In particular, we explore two different approaches for injection: concatenation of pejorative information and substitution of ambiguous words with univocal terms. Our experimental results, both on our corpus and on two popular benchmarks on Italian tweets, show that both approaches lead to a major classification improvement, indicating that word sense disambiguation is a promising preliminary step for misogyny detection. Furthermore, we investigate LLMs' understanding of pejorative epithets by means of contextual word embeddings analysis and prompting.

Related papers

• Language is Scary when Over-Analyzed: Unpacking Implied Misogynistic Reasoning with Argumentation Theory-Driven Prompts [17.259767031006604]
  We propose misogyny detection as an Argumentative Reasoning task. We investigate the capacity of large language models to understand the implicit reasoning used to convey misogyny in both Italian and English.
  arXiv  Detail & Related papers  (2024-09-04T08:27:43Z)
• What an Elegant Bridge: Multilingual LLMs are Biased Similarly in Different Languages [51.0349882045866]
  This paper investigates biases of Large Language Models (LLMs) through the lens of grammatical gender. We prompt a model to describe nouns with adjectives in various languages, focusing specifically on languages with grammatical gender. We find that a simple classifier can not only predict noun gender above chance but also exhibit cross-language transferability.
  arXiv  Detail & Related papers  (2024-07-12T22:10:16Z)
• Measuring Misogyny in Natural Language Generation: Preliminary Results from a Case Study on two Reddit Communities [7.499634046186994]
  We consider the challenge of measuring misogyny in natural language generation. We use data from two well-characterised Incel' communities on Reddit.
  arXiv  Detail & Related papers  (2023-12-06T07:38:46Z)
• The Causal Influence of Grammatical Gender on Distributional Semantics [87.8027818528463]
  How much meaning influences gender assignment across languages is an active area of research in linguistics and cognitive science. We offer a novel, causal graphical model that jointly represents the interactions between a noun's grammatical gender, its meaning, and adjective choice. When we control for the meaning of the noun, the relationship between grammatical gender and adjective choice is near zero and insignificant.
  arXiv  Detail & Related papers  (2023-11-30T13:58:13Z)
• That was the last straw, we need more: Are Translation Systems Sensitive to Disambiguating Context? [64.38544995251642]
  We study semantic ambiguities that exist in the source (English in this work) itself. We focus on idioms that are open to both literal and figurative interpretations. We find that current MT models consistently translate English idioms literally, even when the context suggests a figurative interpretation.
  arXiv  Detail & Related papers  (2023-10-23T06:38:49Z)
• Neighboring Words Affect Human Interpretation of Saliency Explanations [65.29015910991261]
  Word-level saliency explanations are often used to communicate feature-attribution in text-based models. Recent studies found that superficial factors such as word length can distort human interpretation of the communicated saliency scores. We investigate how the marking of a word's neighboring words affect the explainee's perception of the word's importance in the context of a saliency explanation.
  arXiv  Detail & Related papers  (2023-05-04T09:50:25Z)
• Shades of meaning: Uncovering the geometry of ambiguous word representations through contextualised language models [6.760960482418417]
  Lexical ambiguity presents a profound and enduring challenge to the language sciences. Our work offers new insight into psychological understanding of lexical ambiguity through a series of simulations.
  arXiv  Detail & Related papers  (2023-04-26T14:47:38Z)
• The Causal Structure of Semantic Ambiguities [0.0]
  We identify two features: (1) joint plausibility degrees of different possible interpretations, and (2) causal structures according to which certain words play a more substantial role in the processes. We applied this theory to a dataset of ambiguous phrases extracted from Psycholinguistics literature and their human plausibility collected by us.
  arXiv  Detail & Related papers  (2022-06-14T12:56:34Z)
• Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation [20.39599469927542]
  Gender bias is largely recognized as a problematic phenomenon affecting language technologies. Most of current evaluation practices adopt a word-level focus on a narrow set of occupational nouns under synthetic conditions. Such protocols overlook key features of grammatical gender languages, which are characterized by morphosyntactic chains of gender agreement.
  arXiv  Detail & Related papers  (2022-03-18T11:14:16Z)
• Do Context-Aware Translation Models Pay the Right Attention? [61.25804242929533]
  Context-aware machine translation models are designed to leverage contextual information, but often fail to do so. In this paper, we ask several questions: What contexts do human translators use to resolve ambiguous words? We introduce SCAT (Supporting Context for Ambiguous Translations), a new English-French dataset comprising supporting context words for 14K translations. Using SCAT, we perform an in-depth analysis of the context used to disambiguate, examining positional and lexical characteristics of the supporting words.
  arXiv  Detail & Related papers  (2021-05-14T17:32:24Z)
• Speakers Fill Lexical Semantic Gaps with Context [65.08205006886591]
  We operationalise the lexical ambiguity of a word as the entropy of meanings it can take. We find significant correlations between our estimate of ambiguity and the number of synonyms a word has in WordNet. This suggests that, in the presence of ambiguity, speakers compensate by making contexts more informative.
  arXiv  Detail & Related papers  (2020-10-05T17:19:10Z)

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.