A Bayesian account of pronoun and neopronoun acquisition
- URL: http://arxiv.org/abs/2504.02973v1
- Date: Thu, 03 Apr 2025 18:49:08 GMT
- Title: A Bayesian account of pronoun and neopronoun acquisition
- Authors: Cassandra L. Jacobs, Morgan Grobol,
- Abstract summary: We argue for explicitly modeling individual differences in pronoun selection.<n>We present a probabilistic graphical modeling approach based on the nested Chinese Restaurant Franchise Process.<n>We show that such a model can account for variability in how quickly pronouns or names are integrated into symbolic knowledge.
- Score: 10.775624456460063
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: A major challenge to equity among members of queer communities is the use of one's chosen forms of reference, such as personal names or pronouns. Speakers often dismiss their misuses of pronouns as "unintentional", and claim that their errors reflect many decades of fossilized mainstream language use, as well as attitudes or expectations about the relationship between one's appearance and acceptable forms of reference. We argue for explicitly modeling individual differences in pronoun selection and present a probabilistic graphical modeling approach based on the nested Chinese Restaurant Franchise Process (nCRFP) (Ahmed et al., 2013) to account for flexible pronominal reference such as chosen names and neopronouns while moving beyond form-to-meaning mappings and without lexical co-occurrence statistics to learn referring expressions, as in contemporary language models. We show that such a model can account for variability in how quickly pronouns or names are integrated into symbolic knowledge and can empower computational systems to be both flexible and respectful of queer people with diverse gender expression.
Related papers
- Tokens, the oft-overlooked appetizer: Large language models, the distributional hypothesis, and meaning [31.632816425798108]
Tokenization is a necessary component within the current architecture of many language models.
We discuss how tokens and pretraining can act as a backdoor for bias and other unwanted content.
We relay evidence that the tokenization algorithm's objective function impacts the large language model's cognition.
arXiv Detail & Related papers (2024-12-14T18:18:52Z) - UnMASKed: Quantifying Gender Biases in Masked Language Models through
Linguistically Informed Job Market Prompts [0.0]
This research delves into the inherent biases present in masked language models (MLMs)
This study evaluated six prominent models: BERT, RoBERTa, DistilBERT, BERT-multilingual, XLM-RoBERTa, and DistilBERT-multilingual.
The analysis reveals stereotypical gender alignment of all models, with multilingual variants showing comparatively reduced biases.
arXiv Detail & Related papers (2024-01-28T23:00:40Z) - Probabilistic Transformer: A Probabilistic Dependency Model for
Contextual Word Representation [52.270712965271656]
We propose a new model of contextual word representation, not from a neural perspective, but from a purely syntactic and probabilistic perspective.
We find that the graph of our model resembles transformers, with correspondences between dependencies and self-attention.
Experiments show that our model performs competitively to transformers on small to medium sized datasets.
arXiv Detail & Related papers (2023-11-26T06:56:02Z) - VisoGender: A dataset for benchmarking gender bias in image-text pronoun
resolution [80.57383975987676]
VisoGender is a novel dataset for benchmarking gender bias in vision-language models.
We focus on occupation-related biases within a hegemonic system of binary gender, inspired by Winograd and Winogender schemas.
We benchmark several state-of-the-art vision-language models and find that they demonstrate bias in resolving binary gender in complex scenes.
arXiv Detail & Related papers (2023-06-21T17:59:51Z) - MISGENDERED: Limits of Large Language Models in Understanding Pronouns [46.276320374441056]
We evaluate popular language models for their ability to correctly use English gender-neutral pronouns.
We introduce MISGENDERED, a framework for evaluating large language models' ability to correctly use preferred pronouns.
arXiv Detail & Related papers (2023-06-06T18:27:52Z) - Multilingual Conceptual Coverage in Text-to-Image Models [98.80343331645626]
"Conceptual Coverage Across Languages" (CoCo-CroLa) is a technique for benchmarking the degree to which any generative text-to-image system provides multilingual parity to its training language in terms of tangible nouns.
For each model we can assess "conceptual coverage" of a given target language relative to a source language by comparing the population of images generated for a series of tangible nouns in the source language to the population of images generated for each noun under translation in the target language.
arXiv Detail & Related papers (2023-06-02T17:59:09Z) - Welcome to the Modern World of Pronouns: Identity-Inclusive Natural
Language Processing beyond Gender [23.92148222207458]
We provide an overview of 3rd person pronoun issues for Natural Language Processing.
We evaluate existing and novel modeling approaches.
We quantify the impact of a more discrimination-free approach on established benchmark data.
arXiv Detail & Related papers (2022-02-24T06:42:11Z) - Hi, my name is Martha: Using names to measure and mitigate bias in
generative dialogue models [14.624075519580405]
Being trained on real human conversations containing unbalanced gender and race/ethnicity references can lead to models that display learned biases.
We show that several methods of tuning these dialogue models, specifically name scrambling, controlled generation, and unlikelihood training, are effective in reducing bias in conversation.
arXiv Detail & Related papers (2021-09-07T19:20:24Z) - They, Them, Theirs: Rewriting with Gender-Neutral English [56.14842450974887]
We perform a case study on the singular they, a common way to promote gender inclusion in English.
We show how a model can be trained to produce gender-neutral English with 1% word error rate with no human-labeled data.
arXiv Detail & Related papers (2021-02-12T21:47:48Z) - Lexical semantic change for Ancient Greek and Latin [61.69697586178796]
Associating a word's correct meaning in its historical context is a central challenge in diachronic research.
We build on a recent computational approach to semantic change based on a dynamic Bayesian mixture model.
We provide a systematic comparison of dynamic Bayesian mixture models for semantic change with state-of-the-art embedding-based models.
arXiv Detail & Related papers (2021-01-22T12:04:08Z) - Transformer-GCRF: Recovering Chinese Dropped Pronouns with General
Conditional Random Fields [54.03719496661691]
We present a novel framework that combines the strength of Transformer network with General Conditional Random Fields (GCRF) to model the dependencies between pronouns in neighboring utterances.
Results on three Chinese conversation datasets show that the Transformer-GCRF model outperforms the state-of-the-art dropped pronoun recovery models.
arXiv Detail & Related papers (2020-10-07T07:06:09Z)
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.