Scott Hale


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SemEval-2022 Task 8: Multilingual news article similarity
Xi Chen | Ali Zeynali | Chico Camargo | Fabian Flöck | Devin Gaffney | Przemyslaw Grabowicz | Scott Hale | David Jurgens | Mattia Samory
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

Thousands of new news articles appear daily in outlets in different languages. Understanding which articles refer to the same story can not only improve applications like news aggregation but enable cross-linguistic analysis of media consumption and attention. However, assessing the similarity of stories in news articles is challenging due to the different dimensions in which a story might vary, e.g., two articles may have substantial textual overlap but describe similar events that happened years apart. To address this challenge, we introduce a new dataset of nearly 10,000 news article pairs spanning 18 language combinations annotated for seven dimensions of similarity as SemEval 2022 Task 8. Here, we present an overview of the task, the best performing submissions, and the frontiers and challenges for measuring multilingual news article similarity. While the participants of this SemEval task contributed very strong models, achieving up to 0.818 correlation with gold standard labels across languages, human annotators are capable of reaching higher correlations, suggesting space for further progress.

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Hatemoji: A Test Suite and Adversarially-Generated Dataset for Benchmarking and Detecting Emoji-Based Hate
Hannah Kirk | Bertie Vidgen | Paul Rottger | Tristan Thrush | Scott Hale
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Detecting online hate is a complex task, and low-performing models have harmful consequences when used for sensitive applications such as content moderation. Emoji-based hate is an emerging challenge for automated detection. We present HatemojiCheck, a test suite of 3,930 short-form statements that allows us to evaluate performance on hateful language expressed with emoji. Using the test suite, we expose weaknesses in existing hate detection models. To address these weaknesses, we create the HatemojiBuild dataset using a human-and-model-in-the-loop approach. Models built with these 5,912 adversarial examples perform substantially better at detecting emoji-based hate, while retaining strong performance on text-only hate. Both HatemojiCheck and HatemojiBuild are made publicly available.


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Claim Matching Beyond English to Scale Global Fact-Checking
Ashkan Kazemi | Kiran Garimella | Devin Gaffney | Scott Hale
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Manual fact-checking does not scale well to serve the needs of the internet. This issue is further compounded in non-English contexts. In this paper, we discuss claim matching as a possible solution to scale fact-checking. We define claim matching as the task of identifying pairs of textual messages containing claims that can be served with one fact-check. We construct a novel dataset of WhatsApp tipline and public group messages alongside fact-checked claims that are first annotated for containing “claim-like statements” and then matched with potentially similar items and annotated for claim matching. Our dataset contains content in high-resource (English, Hindi) and lower-resource (Bengali, Malayalam, Tamil) languages. We train our own embedding model using knowledge distillation and a high-quality “teacher” model in order to address the imbalance in embedding quality between the low- and high-resource languages in our dataset. We provide evaluations on the performance of our solution and compare with baselines and existing state-of-the-art multilingual embedding models, namely LASER and LaBSE. We demonstrate that our performance exceeds LASER and LaBSE in all settings. We release our annotated datasets, codebooks, and trained embedding model to allow for further research.

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Deciphering Implicit Hate: Evaluating Automated Detection Algorithms for Multimodal Hate
Austin Botelho | Scott Hale | Bertie Vidgen
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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Detecting East Asian Prejudice on Social Media
Bertie Vidgen | Scott Hale | Ella Guest | Helen Margetts | David Broniatowski | Zeerak Waseem | Austin Botelho | Matthew Hall | Rebekah Tromble
Proceedings of the Fourth Workshop on Online Abuse and Harms

During COVID-19 concerns have heightened about the spread of aggressive and hateful language online, especially hostility directed against East Asia and East Asian people. We report on a new dataset and the creation of a machine learning classifier that categorizes social media posts from Twitter into four classes: Hostility against East Asia, Criticism of East Asia, Meta-discussions of East Asian prejudice, and a neutral class. The classifier achieves a macro-F1 score of 0.83. We then conduct an in-depth ground-up error analysis and show that the model struggles with edge cases and ambiguous content. We provide the 20,000 tweet training dataset (annotated by experienced analysts), which also contains several secondary categories and additional flags. We also provide the 40,000 original annotations (before adjudication), the full codebook, annotations for COVID-19 relevance and East Asian relevance and stance for 1,000 hashtags, and the final model.

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Recalibrating classifiers for interpretable abusive content detection
Bertie Vidgen | Scott Hale | Sam Staton | Tom Melham | Helen Margetts | Ohad Kammar | Marcin Szymczak
Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science

We investigate the use of machine learning classifiers for detecting online abuse in empirical research. We show that uncalibrated classifiers (i.e. where the ‘raw’ scores are used) align poorly with human evaluations. This limits their use for understanding the dynamics, patterns and prevalence of online abuse. We examine two widely used classifiers (created by Perspective and Davidson et al.) on a dataset of tweets directed against candidates in the UK’s 2017 general election. A Bayesian approach is presented to recalibrate the raw scores from the classifiers, using probabilistic programming and newly annotated data. We argue that interpretability evaluation and recalibration is integral to the application of abusive content classifiers.


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Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings
Philippa Shoemark | Farhana Ferdousi Liza | Dong Nguyen | Scott Hale | Barbara McGillivray
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Word embeddings are increasingly used for the automatic detection of semantic change; yet, a robust evaluation and systematic comparison of the choices involved has been lacking. We propose a new evaluation framework for semantic change detection and find that (i) using the whole time series is preferable over only comparing between the first and last time points; (ii) independently trained and aligned embeddings perform better than continuously trained embeddings for long time periods; and (iii) that the reference point for comparison matters. We also present an analysis of the changes detected on a large Twitter dataset spanning 5.5 years.

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Challenges and frontiers in abusive content detection
Bertie Vidgen | Alex Harris | Dong Nguyen | Rebekah Tromble | Scott Hale | Helen Margetts
Proceedings of the Third Workshop on Abusive Language Online

Online abusive content detection is an inherently difficult task. It has received considerable attention from academia, particularly within the computational linguistics community, and performance appears to have improved as the field has matured. However, considerable challenges and unaddressed frontiers remain, spanning technical, social and ethical dimensions. These issues constrain the performance, efficiency and generalizability of abusive content detection systems. In this article we delineate and clarify the main challenges and frontiers in the field, critically evaluate their implications and discuss potential solutions. We also highlight ways in which social scientific insights can advance research. We discuss the lack of support given to researchers working with abusive content and provide guidelines for ethical research.