已有研究提出，词语为产出而准备的顺序取决于说话者的语言。在产出“小猫”的翻译对等表达时，德语或荷兰语使用者会在产出的相对较早阶段选择带有性别标记的限定词。法语或意大利语使用者则会推迟限定词或形容词的编码，直到名词的语音形式可用。因此，即使这些词语以相同顺序产出（例如德语的 die kleine Katze、法语的 le petit chat），它们也并非以相同顺序进行计划，并且在产出开始前可能需要不同程度的提前规划。早期选择语言与晚期选择语言之间的这一区分，被提出用于解释如下观察：日耳曼语和斯拉夫语使用者（而非罗曼语使用者）在不同性别的干扰词语境中命名图片时速度更慢。本文开展元分析，以首次直接检验这种跨语言差异，并检验晚期选择假说的一项预测。结果证实，当目标词和干扰词同时呈现时，德语/斯拉夫语中存在性别一致性效应，而罗曼语中不存在该效应。这些元分析并不能证实如下假说：在后一类语言中，当干扰词延迟呈现时会出现类似效应。总体而言，这些分析证实了跨语言差异，但也表明，迄今为止现有证据不足以证实或否定将晚期选择假说作为对该差异的解释。我们强调了未来研究的具体方向。
英文摘要：
It has been proposed that the order in which words are prepared for production depends on the speaker's language. When producing the translation equivalent of the small cat, speakers of German or Dutch select the gender-marked determiner at a relatively early stage of production. Speakers of French or Italian postpone the encoding of a determiner or adjective until the phonological form of the noun is available. Hence, even though the words are produced in the same order (e.g., die kleine Katze in German, le petit chat in French), they are not planned in the same order and might require different amounts of advanced planning prior to production onset. This distinction between early and late selection languages was proposed to account for the observation that speakers of Germanic and Slavic languages, but not of Romance languages, are slower to name pictures in the context of a distractor word of a different gender. Meta-analyses are conducted to provide the first direct test of this cross-linguistic difference and to test a prediction of the late selection hypothesis. They confirm the existence of the gender congruency effect in German/Slavic languages and its absence in Romance languages when target and distractor words are presented simultaneously. They do not allow confirming the hypothesis that in the latter languages, a similar effect emerges when the presentation of the distractor is delayed. Overall, these analyses confirm the cross-linguistic difference but show that the evidence available to date is not sufficient to confirm or reject the late selection hypothesis as an explanation of this difference. We highlight specific directions for future research.
Tilburg University（蒂尔堡大学）；University of Naples L’Orientale（那不勒斯东方大学）
Languages differ in terms of the absence or presence of gender features, the number of gender classes and whether and where gender features are explicitly marked. These cross-linguistic differences can lead to ambiguities that are difficult to resolve, especially for sentence-level MT systems. The identification of ambiguity and its subsequent resolution is a challenging task for which currently there aren't any specific resources or challenge sets available. In this paper, we introduce gENder-IT, an English--Italian challenge set focusing on the resolution of natural gender phenomena by provi…
Lab Dynamics of Language UMR 5596（语言动力学实验室 UMR 5596）；University Lyon 2（里昂第二大学）；Uppsala University（乌普萨拉大学）
We analyze the information provided by the word embeddings about the grammatical gender in Swedish. We wish that this paper may serve as one of the bridges to connect the methods of computational linguistics and general linguistics. Taking nominal classification in Swedish as a case study, we first show how the information about grammatical gender in language can be captured by word embedding models and artificial neural networks. Then, we match our results with previous linguistic hypotheses on assignment and usage of grammatical gender in Swedish and analyze the errors made by the computatio…
We present a corpus-based analysis of the effects of age, gender and region of origin on the production of both "netspeak" or "chatspeak" features and regional speech features in Flemish Dutch posts that were collected from a Belgian online social network platform. The present study shows that combining quantitative and qualitative approaches is essential for understanding non-standard linguistic variation in a CMC corpus. It also presents a methodology that enables the systematic study of this variation by including all non-standard words in the corpus. The analyses resulted in a convincing i…
National Tsing Hua University（国立清华大学）；Mackay Memorial Hospital（马偕纪念医院）；National Chiao Tung University（国立交通大学）
Most previous studies on automatic recognition model for bipolar disorder (BD) were based on both social media and linguistic features. The present study investigates the possibility of adopting only language-based features, namely the syntax and morpheme collocation. We also examine the effect of gender on the results considering gender has long been recognized as an important modulating factor for mental disorders, yet it received little attention in previous linguistic models. The present study collects Twitter posts 3 months prior to the self-disclosure by 349 BD users (231 female, 118 mal…
Measuring, evaluating and reducing Gender Bias has come to the forefront with newer and improved language embeddings being released every few months. But could this bias vary from domain to domain? We see a lot of work to study these biases in various embedding models but limited work has been done to debias Indic languages. We aim to measure and study this bias in Hindi language, which is a higher-order language (gendered) with reference to English, a lower-order language. To achieve this, we study the variations across domains to quantify if domain embeddings allow us some insight into Gende…
University of Pittsburgh（匹兹堡大学）；University of Maryland（马里兰大学）
Multi-party linguistic entrainment refers to the phenomenon that speakers tend to speak more similarly during conversation. We first developed new measures of multi-party entrainment on features describing linguistic style, and then examined the relationship between entrainment and team characteristics in terms of gender composition, team size, and diversity. Next, we predicted the perception of team social outcomes using multi-party linguistic entrainment and team characteristics with a hierarchical regression model. We found that teams with greater gender diversity had higher minimum converg…
This paper addresses the task of user gender classification in social media, with an application to Twitter. The approach automatically predicts gender by leveraging observable information such as the tweet behavior, linguistic content of the user's Twitter feed and the celebrities followed by the user. This paper first evaluates linguistic content based features using LIWC dictionary and popular neighborhood features using Wikipedia and Freebase. Then augments both features which yielded a significant increase in the accuracy for gender prediction. Results show that rich linguistic features c…
Although recent years have brought significant progress in improving translation of unambiguously gendered sentences, translation of ambiguously gendered input remains relatively unexplored. When source gender is ambiguous, machine translation models typically default to stereotypical gender roles, perpetuating harmful bias. Recent work has led to the development of "gender rewriters" that generate alternative gender translations on such ambiguous inputs, but such systems are plagued by poor linguistic coverage. To encourage better performance on this task we present and release GATE, a lingui…
We present a study of the relationship between gender, linguistic style, and social networks, using a novel corpus of 14,000 Twitter users. Prior quantitative work on gender often treats this social variable as a female/male binary; we argue for a more nuanced approach. By clustering Twitter users, we find a natural decomposition of the dataset into various styles and topical interests. Many clusters have strong gender orientations, but their use of linguistic resources sometimes directly conflicts with the population-level language statistics. We view these clusters as a more accurate reflect…
Imagine a princess asleep in a castle, waiting for her prince to slay the dragon and rescue her. Tales like the famous Sleeping Beauty clearly divide up gender roles. But what about more modern stories, borne of a generation increasingly aware of social constructs like sexism and racism? Do these stories tend to reinforce gender stereotypes, or counter them? In this paper, we present a technique that combines natural language processing with a crowdsourced lexicon of stereotypes to capture gender biases in fiction. We apply this technique across 1.8 billion words of fiction from the Wattpad on…
CENIDET（墨西哥国家数字教育技术中心）；COLMEX（墨西哥学院）；IIM（印度管理研究所）；Université de Montréal（蒙特利尔大学）；EBSI（图书馆与信息科学学院）；Université d’Avignon（阿维尼翁大学）；LIA（阿维尼翁计算机科学实验室）
This work aims to evaluate the ability that both probabilistic and state-of-the-art vector space modeling (VSM) methods provide to well known machine learning algorithms to identify social network documents to be classified as aggressive, gender biased or communally charged. To this end, an exploratory stage was performed first in order to find relevant settings to test, i.e. by using training and development samples, we trained multiple algorithms using multiple vector space modeling and probabilistic methods and discarded the less informative configurations. These systems were submitted to t…
The relation between language and thought has occupied linguists for at least a century. Neo-Whorfianism, a weak version of the controversial Sapir-Whorf hypothesis, holds that our thoughts are subtly influenced by the grammatical structures of our native language. One area of investigation in this vein focuses on how the grammatical gender of nouns affects the way we perceive the corresponding objects. For instance, does the fact that key is masculine in German (der Schlüssel), but feminine in Spanish (la llave) change the speakers' views of those objects? Psycholinguistic evidence presented …
National Research Council of Italy（意大利国家研究委员会）；Institute of Intelligent Systems for Automation – Genoa branch（智能系统自动化研究所热那亚分部）；Institute of Electronics, Computer and Telecommunication Engineering – Genoa branch（电子、计算机与通信工程研究所热那亚分部）
The purpose of this study is to find evidence for supporting the hypothesis that language is the mirror of our thinking, our prejudices and cultural stereotypes. In this analysis, a questionnaire was administered to 537 people. The answers have been analysed to see if gender stereotypes were present such as the attribution of psychological and behavioural characteristics. In particular, the aim was to identify, if any, what are the stereotyped images, which emerge in defining the roles of men and women in modern society. Moreover, the results given can be a good starting point to understand if…
Individuals signal aspects of their identity and beliefs through linguistic choices. Studying these choices in aggregate allows us to examine large-scale attitude shifts within a population. Here, we develop computational methods to study word choice within a sociolinguistic lexical variable -- alternate words used to express the same concept -- in order to test for change in the United States towards sexuality and gender. We examine two variables: i) referents to significant others, such as the word "partner" and ii) referents to an indefinite person, both of which could optionally be marked …
University of Bristol（布里斯托大学）；National Research University Higher School of Economics（高等经济学院国立研究大学）
LSTMs trained on next-word prediction can accurately perform linguistic tasks that require tracking long-distance syntactic dependencies. Notably, model accuracy approaches human performance on number agreement tasks (Gulordava et al., 2018). However, we do not have a mechanistic understanding of how LSTMs perform such linguistic tasks. Do LSTMs learn abstract grammatical rules, or do they rely on simple heuristics? Here, we test gender agreement in French which requires tracking both hierarchical syntactic structures and the inherent gender of lexical units. Our model is able to reliably pred…
From 'Showgirls' to 'Performers': Fine-tuning with Gender-inclusive Language for Bias Reduction in LLMs
Insight SFI Research Centre for Data Analytics（Insight SFI数据 analytics研究中心）；School of Information and Communication Studies（信息与传播研究学院）；University College Dublin（都柏林大学学院）
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；University of Toronto（多伦多大学）；Academia Sinica（中央研究院）
Chinese dynastic histories form a large continuous linguistic space of approximately 2000 years, from the 3rd century BCE to the 18th century CE. The histories are documented in Classical (Literary) Chinese in a corpus of over 20 million characters, suitable for the computational analysis of historical lexicon and semantic change. However, there is no freely available open-source corpus of these histories, making Classical Chinese low-resource. This project introduces a new open-source corpus of twenty-four dynastic histories covered by Creative Commons license. An original list of Classical C…
University of Toronto（多伦多大学）；Bar-Ilan University（巴伊兰大学）
A large body of research on gender-linked language has established foundations regarding cross-gender differences in lexical, emotional, and topical preferences, along with their sociological underpinnings. We compile a novel, large and diverse corpus of spontaneous linguistic productions annotated with speakers' gender, and perform a first large-scale empirical study of distinctions in the usage of \textit{figurative language} between male and female authors. Our analyses suggest that (1) idiomatic choices reflect gender-specific lexical and semantic preferences in general language, (2) men's…
Decades of research on differences in the language of men and women have established postulates about preferences in lexical, topical, and emotional expression between the two genders, along with their sociological underpinnings. Using a novel dataset of male and female linguistic productions collected from the Reddit discussion platform, we further confirm existing assumptions about gender-linked affective distinctions, and demonstrate that these distinctions are amplified in social media postings involving emotionally-charged discourse related to COVID-19. Our analysis also confirms consider…
taz2024full: Analysing German Newspapers for Gender Bias and Discrimination across Decades
Faculty for Computer Science and Mathematics, Hochschule München University of Applied Sciences（计算机科学与数学系，慕尼黑应用科学大学）；Department of Statistics, LMU Munich（统计学系，慕尼黑大学）；Munich Center for Machine Learning (MCML), LMU Munich（慕尼黑机器学习中心（MCML），慕尼黑大学）
Fondazione Bruno Kessler（布鲁诺·凯塞勒基金会）；University of Trento（特伦托大学）
Having recognized gender bias as a major issue affecting current translation technologies, researchers have primarily attempted to mitigate it by working on the data front. However, whether algorithmic aspects concur to exacerbate unwanted outputs remains so far under-investigated. In this work, we bring the analysis on gender bias in automatic translation onto a seemingly neutral yet critical component: word segmentation. Can segmenting methods influence the ability to translate gender? Do certain segmentation approaches penalize the representation of feminine linguistic markings? We address …
University of Trento（特伦托大学）；Fondazione Bruno Kessler（布鲁诺·凯塞勒基金会）
Gender bias is largely recognized as a problematic phenomenon affecting language technologies, with recent studies underscoring that it might surface differently across languages. However, 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, marked on a variety of lexical items and parts-of-speech (POS). To overcome this limitation, we enrich the natural, gender-sensitive MuST-SHE …
Tilburg University（蒂尔堡大学）；University of Maryland, College Park（马里兰大学帕克分校）
Recent studies in the field of Machine Translation (MT) and Natural Language Processing (NLP) have shown that existing models amplify biases observed in the training data. The amplification of biases in language technology has mainly been examined with respect to specific phenomena, such as gender bias. In this work, we go beyond the study of gender in MT and investigate how bias amplification might affect language in a broader sense. We hypothesize that the 'algorithmic bias', i.e. an exacerbation of frequently observed patterns in combination with a loss of less frequent ones, not only exace…
The vector representation of words, known as word embeddings, has opened a new research approach in linguistic studies. These representations can capture different types of information about words. The grammatical gender of nouns is a typical classification of nouns based on their formal and semantic properties. The study of grammatical gender based on word embeddings can give insight into discussions on how grammatical genders are determined. In this study, we compare different sets of word embeddings according to the accuracy of a neural classifier determining the grammatical gender of nouns…
University of Groningen（格罗宁根大学）；University of Malta（马耳他大学）
Contextualized word embeddings have been replacing standard embeddings as the representational knowledge source of choice in NLP systems. Since a variety of biases have previously been found in standard word embeddings, it is crucial to assess biases encoded in their replacements as well. Focusing on BERT (Devlin et al., 2018), we measure gender bias by studying associations between gender-denoting target words and names of professions in English and German, comparing the findings with real-world workforce statistics. We mitigate bias by fine-tuning BERT on the GAP corpus (Webster et al., 2018…
Social media is considered a democratic space in which people connect and interact with each other regardless of their gender, race, or any other demographic aspect. Despite numerous efforts that explore demographic aspects in social media, it is still unclear whether social media perpetuates old inequalities from the offline world. In this dissertation, we attempt to identify gender and race of Twitter users located in the United States using advanced image processing algorithms from Face++. We investigate how different demographic groups connect with each other and differentiate them regardi…
Università Luigi Bocconi（博科尼大学）；University of South Carolina（南卡罗来纳大学）
The world of pronouns is changing. From a closed class of words with few members to a much more open set of terms to reflect identities. However, Natural Language Processing (NLP) is barely reflecting this linguistic shift, even though recent work outlined the harms of gender-exclusive language technology. Particularly problematic is the current modeling 3rd person pronouns, as it largely ignores various phenomena like neopronouns, i.e., pronoun sets that are novel and not (yet) widely established. This omission contributes to the discrimination of marginalized and underrepresented groups, e.g…
School of Interactive Computing（交互计算学院）；Georgia Institute of Technology（佐治亚理工学院）
Twitter is often used in quantitative studies that identify geographically-preferred topics, writing styles, and entities. These studies rely on either GPS coordinates attached to individual messages, or on the user-supplied location field in each profile. In this paper, we compare these data acquisition techniques and quantify the biases that they introduce; we also measure their effects on linguistic analysis and text-based geolocation. GPS-tagging and self-reported locations yield measurably different corpora, and these linguistic differences are partially attributable to differences in dat…
Cross-Lingual Probing and Community-Grounded Analysis of Gender Bias in Low-Resource Bengali
Department of Computer Science（计算机科学系）；AIUB, Dhaka, Bangladesh；Department of Information Technology（信息科技系）；Wilmington University, Delaware, USA；WUST, Virginia, USA；Faculty of Information Science（信息科学系）；Technology Multimedia University, Melaka, Malaysia（技术多媒体大学, Melaka, Malaysia）；Centre for Intelligent Cloud Computing（智能云计算中心）
Beyond Content: How Grammatical Gender Shapes Visual Representation in Text-to-Image Models
MBZUAI（穆罕默德·本·扎耶德人工智能大学）；Vector Institute of AI（Vector人工智能研究所）；University of Central Florida（中佛罗里达大学）；University of British Columbia（不列颠哥伦比亚大学）；Emory University（埃默里大学）；Invertible AI
How desirable is alignment between LLMs and linguistically diverse human users?
Humboldt-Universität zu Berlin（柏林洪堡大学）；Berlin School of Mind and Brain（柏林心智与大脑学院）；Einstein Center for Neurosciences Berlin（柏林爱因斯坦神经科学中心）；Technische Universität Berlin（柏林工业大学）；German Research Center for Artificial Intelligence（德国人工智能研究中心）
Akal Badi ya Bias: An Exploratory Study of Gender Bias in Hindi Language Technology
Microsoft Research（微软研究院）；Karya；Carnegie Mellon University（卡内基梅隆大学）；University of Michigan（密歇根大学）；Delft University of Technology（代尔夫特理工大学）；Cornell University（康奈尔大学）
Mind the Inclusivity Gap: Multilingual Gender-Neutral Translation Evaluation with mGeNTE
Fondazione Bruno Kessler（布鲁诺·科塞尔基金会）；Instituto de Telecomunicações（电信研究所）；DIT Forlì - University of Bologna（福利大学迪提学院）；Data Science Group - University of Hamburg（汉堡大学数据科学组）；University of Trento（特伦托大学）；LT 3 - Ghent University（根特大学LT 3）
