SocioProbe: What, When, and Where Language Models Learn about Sociodemographics
December, 2022
Pre-trained language models (PLMs) have outperformed other NLP models on a wide range of tasks. Opting for a more thorough …
Bridging Fairness and Environmental Sustainability in Natural Language Processing
December, 2022
Fairness and environmental impact are important research directions for the sustainable development of artificial intelligence. …
Measuring Harmful Representations in Scandinavian Language Models
December, 2022
Scandinavian countries are perceived as role-models when it comes to gender equality. With the advent of pre-trained language models …
Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale
November, 2022
Machine learning models are now able to convert user-written text descriptions into naturalistic images. These models are available to …
Data-Efficient Strategies for Expanding Hate Speech Detection into Under-Resourced Languages
October, 2022
Hate speech is a global phenomenon, but most hate speech datasets so far focus on English-language content. This hinders the …
The State of Profanity Obfuscation in Natural Language Processing Scientific Publications
October, 2022
Work on hate speech has made the consideration of rude and harmful examples in scientific publications inevitable. This raises various …
Is It Worth the (Environmental) Cost? Limited Evidence for the Benefits of Diachronic Continuous Training
October, 2022
Language is constantly changing and evolving, leaving language models to quickly become outdated, both factually and linguistically. …
Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender
October, 2022
The world of pronouns is changing – from a closed word class with few members to an open set of terms to reflect identities. However, …
Guiding the Release of Safer E2E Conversational AI through Value Sensitive Design
September, 2022
Over the last several years, end-to-end neural conversational agents have vastly improved their ability to carry unrestricted, …
ferret: a Framework for Benchmarking Explainers on Transformers
August, 2022
Many interpretability tools allow practitioners and researchers to explain Natural Language Processing systems. However, each tool …
Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models
July, 2022
Hate speech detection models are typically evaluated on held-out test sets. However, this risks painting an incomplete and potentially …
HATE-ITA: Hate Speech Detection in Italian Social Media Text
July, 2022
Online hate speech is a dangerous phenomenon that can (and should) be promptly counteracted properly. While Natural Language Processing …
Hard and Soft Evaluation of NLP models with BOOtSTrap SAmpling - BooStSa
May, 2022
Natural Language Processing (NLP) ‘s applied nature makes it necessary to select the most effective and robust models. Producing …
MilaNLP at SemEval-2022 Task 5: Using Perceiver IO for Detecting Misogynous Memes with Text and Image Modalities
April, 2022
In this paper, we describe the system proposed by the MilaNLP team for the Multimedia Automatic Misogyny Identification (MAMI) …
Language Invariant Properties in Natural Language Processing
April, 2022
Meaning is context-dependent, but many properties of language (should) remain the same even if we transform the context. For example, …
XLM-EMO: Multilingual Emotion Prediction in Social Media Text
April, 2022
Detecting emotion in text allows social and computational scientists to study how people behave and react to online events. However, …
Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks
April, 2022
Labelled data is the foundation of most natural language processing tasks. However, labelling data is difficult and there often are …
Pipelines for Social Bias Testing of Large Language Models
April, 2022
The maturity level of language models is now at a stage in which many companies rely on them to solve various tasks. However, while …
Measuring Harmful Sentence Completion in Language Models for LGBTQIA+ Individuals
April, 2022
Current language technology is ubiquitous and directly influences individuals' lives worldwide. Given the recent trend in AI on …
Benchmarking Post-Hoc Interpretability Approaches for Transformer-based Misogyny Detection
April, 2022
Transformer-based Natural Language Processing models have become the standard for hate speech detection. However, the unconscious use …
Fair and Argumentative Language Modeling for Computational Argumentation
April, 2022
Although much work in NLP has focused on measuring and mitigating stereotypical bias in semantic spaces, research addressing bias in …
DS-TOD: Efficient Domain Specialization for Task Oriented Dialog
April, 2022
Recent work has shown that self-supervised dialog-specific pretraining on large conversational datasets yields substantial gains over …
SAFETYKIT: First Aid for Measuring Safety in Open-domain Conversational Systems
March, 2022
The social impact of natural language processing and its applications has received increasing attention. In this position paper, we …
Entropy-based Attention Regularization Frees Unintended Bias Mitigation from Lists
March, 2022
Natural Language Processing (NLP) models risk overfitting to specific terms in the training data, thereby reducing their performance, …
Text Analysis in Python for Social Scientists – Prediction and Classification
January, 2022
Text contains a wealth of information about about a wide variety of sociocultural constructs. Automated prediction methods can infer …
Learning from Disagreement: A Survey
December, 2021
Many tasks in Natural Language Processing (NLP) and Computer Vision (CV) offer evidence that humans disagree, from objective tasks such …
Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence
August, 2021
Topic models extract groups of words from documents, whose interpretation as a topic hopefully allows for a better understanding of the …
On the Gap between Adoption and Understanding in NLP
August, 2021
There are some issues with current research trends in NLP that can hamper the free development of scientific research. We identify five …
Five sources of bias in natural language processing
August, 2021
Recently, there has been an increased interest in demographically grounded bias in natural language processing (NLP) applications. Much …
Exposing the limits of Zero-shot Cross-lingual Hate Speech Detection
August, 2021
Reducing and counter-acting hate speech on Social Media is a significant concern. Most of the proposed automatic methods are conducted …
'We will Reduce Taxes' - Identifying Election Pledges with Language Models
August, 2021
In an election campaign, political parties pledge to implement various projects–should they be elected. But do they follow …
The Importance of Modeling Social Factors of Language: Theory and Practice
June, 2021
Natural language processing (NLP) applications are now more powerful and ubiquitous than ever before. With rapidly developing (neural) …
Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction
June, 2021
We investigate grounded language learning through real-world data, by modelling a teacher-learner dynamics through the natural interactions occurring between users and search engines.
MilaNLP @ WASSA: Does BERT Feel Sad When You Cry?
May, 2021
The paper describes the MilaNLP team’s submission (Bocconi University, Milan) in the WASSA 2021 Shared Task on Empathy Detection and …
FEEL-IT: Emotion and Sentiment Classification for the Italian Language
May, 2021
Sentiment analysis is a common task to understand people’s reactions online. Still, we often need more nuanced information: is …
Beyond Black & White: Leveraging Annotator Disagreement via Soft-Label Multi-Task Learning
May, 2021
Supervised learning assumes that a ground truth label exists. However, the reliability of this ground truth depends on human …
Universal Joy A Data Set and Results for Classifying Emotions Across Languages
April, 2021
While emotions are universal aspects of human psychology, they are expressed differently across different languages and cultures. We …
BERTective: Language Models and Contextual Information for Deception Detection
April, 2021
Spotting a lie is challenging but has an enormous potential impact on security as well as private and public safety. Several NLP …
Cross-lingual Contextualized Topic Models with Zero-shot Learning
March, 2021
We introduce a novel topic modeling method that can make use of contextulized embeddings (e.g., BERT) to do zero-shot cross-lingual topic modeling.
Text Analysis in Python for Social Scientists – Discovery and Exploration
December, 2020
Text is everywhere, and it is a fantastic resource for social scientists. However, because it is so abundant, and because language is …
“You Sound Just Like Your Father” Commercial Machine Translation Systems Include Stylistic Biases
July, 2020
The main goal of machine translation has been to convey the correct content. Stylistic considerations have been at best secondary. We …
Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview
July, 2020
An increasing number of natural language processing papers address the effect of bias on predictions, introducing mitigation techniques …
Visualizing Regional Language Variation Across Europe on Twitter
March, 2020
Geotagged Twitter data allows us to investigate correlations of geographic language variation, both at an interlingual and intralingual …
What the [MASK]? Making Sense of Language-Specific BERT Models
March, 2020
Recently, Natural Language Processing (NLP) has witnessed an impressive progress in many areas, due to the advent of novel, pretrained …
Helpful or Hierarchical? Predicting the Communicative Strategies of Chat Participants, and their Impact on Success
March, 2020
When interacting with each other, we motivate, advise, inform, show love or power towards our peers. However, the way we interact may …
Fake opinion detection: how similar are crowdsourced datasets to real data?
January, 2020
Identifying deceptive online reviews is a challenging tasks for Natural Language Processing (NLP). Collecting corpora for the task is …
A Case for Soft Loss Functions
January, 2020
Recently, Peterson et al. provided evidence of the benefits of using probabilistic soft labels generated from crowd annotations for …
Identifying Linguistic Areas for Geolocation
November, 2019
Geolocating social media posts relies on the assumption that language carries sufficient geographic information. However, locations are …
Hey Siri. Ok Google. Alexa: A topic modeling of user reviews for smart speakers
November, 2019
User reviews provide a significant source of information for companies to understand their market and audience. In order to discover …
Geolocation with Attention-Based Multitask Learning Models
November, 2019
Geolocation, predicting the location of a post based on text and other information, has a huge potential for several social media …
Dense Node Representation for Geolocation
November, 2019
Prior research has shown that geolocation can be substantially improved by including user network information. While effective, it …
Women’s Syntactic Resilience and Men’s Grammatical Luck: Gender-Bias in Part-of-Speech Tagging and Dependency Parsing
July, 2019
Several linguistic studies have shown the prevalence of various lexical and grammatical patterns in texts authored by a person of a …
Peer networks and entrepreneurship: A Pan-African RCT
January, 2019
Can large-scale peer interaction foster entrepreneurship and innovation? We conducted an RCT involving almost 5,000 entrepreneurs from …
Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning
October, 2018
Newspapers need to attract readers with headlines, anticipating their readers’ preferences. These preferences rely on topical, …
Comparing Bayesian Models of Annotation
October, 2018
The analysis of crowdsourced annotations in natural language processing is concerned with identifying (1) gold standard labels, (2) …
Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting
October, 2018
Dialects are one of the main drivers of language variation, a major challenge for natural language processing tools. In most languages, …
The Social and the Neural Network: How to Make Natural Language Processing about People again
June, 2018
Over the years, natural language processing has increasingly focused on tasks that can be solved by statistical models, but ignored the …
