United Arab Emirates

Socioeconomic status (SES) is a metric used to compare a person&#39;s social standing based on their income, level of education, and occupation. Students from low SES backgrounds are those whose parents have low income and have limited access to the resources and opportunities they need to aid their success. Researchers have studied many issues and solutions for students with low SES, and there is a lot of research going on in many fields, especially in the social sciences. Computer science, however, has not yet as a field turned its considerable potential to addressing these inequalities. Utilizing Natural Language Processing (NLP) methods and technology, our work aims to address these disparities and ways to bridge the gap. We built a simple string matching algorithm including Latent Dirichlet Allocation (LDA) topic model and Open Information Extraction (open IE) to generate relational triples that are connected to the context of the students&#39; challenges, and the strategies they follow to overcome them. We manually collected 16 narratives about the experiences of low SES students in higher education from a publicly accessible internet forum (Reddit) and tested our model on them. We demonstrate that our strategy is effective (from 37.50\% to 80\%) in gathering contextual data about low SES students, in particular, about their difficulties while in a higher educational institution and how they improve their situation. A detailed error analysis suggests that increase of data, improvement of the LDA model, and quality of triples can help get better results from our model. For the advantage of other researchers, we make our code available.

EMNLP 2022

Impacts of Low Socio-economic Status on Educational Outcomes: A Narrative Based Analysis

low ses students

narrative analysis

topic modelling

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**Dear Colleagues,**

Welcome to the 43rd Annual Conference of the Cognitive Science Society! 

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CogSci 2021

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education. The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

Socioeconomic status (SES) is a metric used to compare a person's social standing based on their income, level of education, and occupation. Students from low SES backgrounds are those whose parents have low income and have limited access to the resources and opportunities they need to aid their success. Researchers have studied many issues and solutions for students with low SES, and there is a lot of research going on in many fields, especially in the social sciences. Computer science, however, has not yet as a field turned its considerable potential to addressing these inequalities. Utilizing Natural Language Processing (NLP) methods and technology, our work aims to address these disparities and ways to bridge the gap. We built a simple string matching algorithm including Latent Dirichlet Allocation (LDA) topic model and Open Information Extraction (open IE) to generate relational triples that are connected to the context of the students' challenges, and the strategies they follow to overcome them. We manually collected 16 narratives about the experiences of low SES students in higher education from a publicly accessible internet forum (Reddit) and tested our model on them. We demonstrate that our strategy is effective (from 37.50\% to 80\%) in gathering contextual data about low SES students, in particular, about their difficulties while in a higher educational institution and how they improve their situation. A detailed error analysis suggests that increase of data, improvement of the LDA model, and quality of triples can help get better results from our model. For the advantage of other researchers, we make our code available.

workshop paper

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EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

Essentialist beliefs (i.e., believing that members of the same group are fundamentally alike) play a central role in social stereotypes and can lead to harm when left unchallenged. In our work, we conduct exploratory studies into the task of countering essentialist beliefs (e.g., "liberals are stupid"). Drawing on prior work from psychology and NLP, we construct five types of counterstatements and conduct human studies on the effectiveness of these different strategies. Our studies also investigate the role in choosing a counterstatement of the level of explicitness with which an essentialist belief is conveyed. We find that statements that broaden the scope of a stereotype (e.g., to other groups, as in "conservatives can also be stupid") are the most popular countering strategy. We conclude with a discussion of challenges and open questions for future work in this area (e.g., improving factuality, studying community-specific variation) and we emphasize the importance of work at the intersection of NLP and psychology.

Towards Countering Essentialism through Social Bias Reasoning

The topic of Climate Change (CC) has received limited attention in NLP despite its real-world urgency. Activists and policy-makers need NLP tools in order to effectively process the vast and rapidly growing textual data produced on CC. Their utility, however, primarily depends on whether state-of-the-art models can generalize across various tasks in the CC domain. In order to address this gap, we introduce the Climate Change Benchmark (ClimaBench), a benchmark collection of existing disparate datasets for evaluating model performance across a diverse set of CC NLU tasks systematically. Further, we enhance the benchmark by releasing two large-scale labelled text classification and question-answering datasets curated from publicly available environmental disclosures. Lastly, we provide an analysis of several generic and CC-oriented models answering whether fine-tuning on domain text offers any improvements across these tasks. We hope this work provides a standard assessment tool for research on CC text data.

ClimaBench: A Benchmark Dataset For Climate Change Text Understanding in English

Social media has become an important information source for crisis management and provides quick access to ongoing developments and critical information. However, classification models suffer from event-related biases and highly imbalanced label distributions which still poses a challenging task. To address these challenges, we propose a combination of entity-masked language modeling and hierarchical multi-label classification as a multi-task learning problem. We evaluate our method on tweets from the TREC-IS dataset and show an absolute performance gain w.r.t. F1-score of up to 10\% for actionable information types. Moreover, we found that entity-masking reduces the effect of overfitting to in-domain events and enables improvements in cross-event generalization.

Enhancing Crisis-Related Tweet Classification with Entity-Masked Language Modeling and Multi-Task Learning

Creating classifiers of disinformation is time-consuming, expensive, and requires vast effort from experts spanning different fields. Even when these efforts succeed, their roll-out to publicly available applications stagnates. While these models struggle to find their consumer-accessible use, disinformation behavior online evolves at a pressing speed. The hoaxes get shared in various abbreviations on social networks, often in user-restricted areas, making external monitoring and intervention virtually impossible. To re-purpose existing NLP methods for the new paradigm of sharing misinformation, we propose leveraging information about given texts' originating news sources to proxy the respective text's trustworthiness. We first present a methodology for determining the sources' overall credibility. We demonstrate our pipeline construction in a specific language and introduce CNSC: a novel dataset for Czech articles' news source and source credibility classification. We constitute initial benchmarks on multiple architectures. Lastly, we create in-the-wild wrapper applications of the trained models: a chatbot, a browser extension, and a standalone web application.

Misinformation Detection in the Wild: News Source Classification as a Proxy for Non-article Texts

It is important to understand how people use words to persuade each other. This helps understand debate, and detect persuasive narratives in regard to e.g. misinformation. While computational modelling of some aspects of persuasion has received some attention, a way to unify and describe the overall phenomenon of when persuasion becomes undesired and problematic, is missing. In this paper, we attempt to address this by proposing a taxonomy of computational persuasion. Drawing upon existing research and resources, this paper shows how to re-frame and re-organise current work into a coherent framework targeting the misuse of rhetorical appeals. As a study to validate these re-framings, we then train and evaluate models of persuasion adapted to our taxonomy. Our results show an application of our taxonomy, and we are able to detecting misuse of rhetorical appeals, finding that these are more often used in misinformative contexts than in true ones.

Modelling Persuasion through Misuse of Rhetorical Appeals

With the identification of the inequality encoded in information acquisition among social classes, we propose to leverage a powerful concept that has never been studied as a linguistic construct, "fun", to deconstruct the inequality. Inspired by theories in sociology, we draw connection between social class and information cocoon, through the lens of fun, and hypothesize the measurement of "how fun one's dominating social cocoon is" to be an indicator of the social class of an individual. Following this, we propose an NLP framework to combat the issue by measuring how fun one's information cocoon is, and empower individuals to emancipate from their trapped cocoons. We position our work to be a domain-agnostic framework that can be deployed in a lot of downstream cases, and is one that aims to deconstruct, as opposed to reinforcing, the traditional social structure of beneficiaries.

Breaking through Inequality of Information Acquisition among Social Classes: A Modest Effort on Measuring "Fun"

We present a web application for creating games and exercises for teaching English as a foreign language with the help of NLP tools. The application contains different kinds of games such as crosswords, word searches, a memory game, and a multiplayer game based on the classic battleship pen and paper game. This application was built with the aim of supporting teachers in rural schools that are teaching English lessons, so they can easily create interactive and engaging activities for their students. We present the context and history of the project, the current state of the web application, and some ideas on how we will expand it in the future.

Using NLP to Support English Teaching in Rural Schools

Providing the right amount of explanation in an employment interview can help the interviewee effectively communicate their skills and experience to the interviewer and convince the she/he is the right candidate for the job. This paper examines natural language processing (NLP) approaches, including word-based tokenization, lexicon-based representations, and pre-trained embeddings with deep learning models, for detecting the degree of explanation in a job interview response. These are exemplified in a study of 24 military veterans who are the focal group of this study, since they can experience unique challenges in job interviews due to the unique verbal communication style that is prevalent in the military. Military veterans conducted mock interviews with industry recruiters and data from these interviews were transcribed and analyzed. Results indicate that  the feasibility of automated NLP methods for detecting the degree of explanation in an interview response. Features based on tokenizer analysis are the most effective in detecting under-explained responses (i.e., 0.29 F1-score), while lexicon-based methods depict the higher performance in detecting over-explanation (i.e., 0.51 F1-score). Findings from this work lay the foundation for the design of intelligent assistive technologies that can provide personalized learning pathways to job candidates, especially those belonging to sensitive or under-represented populations, and helping them succeed in employment job interviews, ultimately contributing to an inclusive workforce.

"Am I Answering My Job Interview Questions Right?'': A NLP Approach to Predict Degree of Explanation in Job Interview Responses

Patronizing and condescending language is characterized, among others, by its subtle nature. It thus seems reasonable to assume that detecting condescending language in text would be harder than detecting more explicitly harmful language, such as hate speech. However, the results of a SemEval-2022 Task devoted to this topic paint a different picture, with the top-performing systems achieving remarkably strong results. In this paper, we analyse the surprising effectiveness of standard text classification methods in more detail. In particular, we highlight the presence of two rather different types of condescending language in the dataset from the SemEval task. Some inputs are condescending because of the way they talk about a particular subject, i.e. condescending language in this case is a linguistic phenomenon, which can, in principle, be learned from training examples. However, other inputs are condescending because of the nature of what is said, rather than the way in which it is expressed, e.g. by emphasizing stereotypes about a given community. In such cases, our ability to detect condescending language, with current methods, largely depends on the presence of similar examples in the training data.

Identifying Condescending Language: A Tale of Two Distinct Phenomena?

A young child's vocabulary size is correlated with their level of personal wellbeing and future academic success. Yet, interventions aimed at increasing early vocabulary would ideally be tailored to each individual child's needs and interests, and such personalization would be impossible without technological support. Here, we explore if and how natural language processing can be used to create individualized bedtime stories around target words to be learned by preschoolers. Generating stories from scratch is challenging and often results in stories of low quality. Thus, we propose an alternative approach: completing phrase-level gaps within prewritten stories. On this task, we explore the performance of GPT-3 with and without finetuning as well as with and without providing a word which is semantically related to the target word. Manual evaluation of the generated stories shows that GPT-3 and GPT-3-based models perform well on the task. Using GPT-3 without finetuning and including a context word into the prompt is the best performing approach.

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Next from EMNLP 2022

Towards Countering Essentialism through Social Bias Reasoning

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Impact of Socio-Economic Status on Cognitive Processing