Papers by Gavin Abercrombie
ParlVote: A Corpus for Sentiment Analysis of Political Debates (2020.lrec-1)
Copied to clipboard
| Challenge: | Debate transcripts from the UK Parliament contain information about the positions taken by politicians towards important topics, but are difficult for humans to process manually. |
| Approach: | They propose to use a linear classifier and a transformer word embedding model to classify sentiment polarity in debate speeches to evaluate sentiment analysis systems for the political domain. |
| Outcome: | The proposed method performs better on the largest dataset and is more robust to other datasets. |
Mirages. On Anthropomorphism in Dialogue Systems (2023.emnlp-main)
Copied to clipboard
| Challenge: | Automated dialogue systems are anthropomorphised by developers and personified by users. |
| Approach: | They propose to examine linguistic factors that contribute to the anthropomorphism of dialogue systems and the harms that can arise thereof. |
| Outcome: | The proposed systems are anthropomorphised and personified by users . linguistic factors can also be used to reinforce gender stereotypes and conceptions of acceptable language. |
Re-examining Sexism and Misogyny Classification with Annotator Attitudes (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing datasets for content moderation fail to capture plurality of possible annotator perspectives or ensure representation of affected groups. |
| Approach: | They examine the relationship between annotator identities and attitudes and the responses they give to two GBV labelling tasks. |
| Outcome: | The results show that higher Right Wing Authoritarianism scores are associated with a higher propensity to label text as sexist . higher scores are also associated with negative attitudes towards sexism and neosexist attitudes . |
Risk-graded Safety for Handling Medical Queries in Conversational AI (2022.aacl-short)
Copied to clipboard
| Challenge: | Conversational AI systems can engage in unsafe behaviour when handling medical queries that could lead to death. |
| Approach: | They label medical queries with crowdsourced and expert annotations to identify the seriousness of the prompts and recognise the risk types posed by the responses. |
| Outcome: | The results suggest that these tasks can be automated, but caution should be exercised, as errors can potentially be very serious. |
NLP for Counterspeech against Hate and Misinformation (CSHAM) (2025.acl-tutorials)
Copied to clipboard
| Challenge: | tutorial aims to show how counterspeech is used to tackle abuse and misinformation by individuals, activists and organisations. |
| Approach: | tutorial aims to show how counterspeech is currently used to tackle abuse and misinformation . will also show how Natural Language Processing (NLP) and Generation (NLG) can be applied to automate its production. |
| Outcome: | The tutorial will bring diverse multidisciplinary perspectives to safety research . case studies from industry and public policy will be included . |
SafetyKit: First Aid for Measuring Safety in Open-domain Conversational Systems (2022.acl-long)
Copied to clipboard
| Challenge: | Several studies discuss the potential harms and benefits of large language models (LLMs) large neural models can replicate and even amplify negative, stereotypical, and derogatory associations in the data. |
| Approach: | They propose to use a first aid kit to assess the safety of conversational AI in various settings . they propose several future directions and discuss ethical considerations . |
| Outcome: | The proposed tools can provide estimates of the relative safety of systems in various settings, but they still have several shortcomings. |
Counterspeech Generation using Small Language Models (2026.acl-srw)
Copied to clipboard
| Challenge: | Social media use is growing annually with about 68.5% of the global population active on these platforms as of July 2025. |
| Approach: | They evaluate SLMs ranging from 100 million to 3 billion parameters using simple prompting strategies as well as fine-tuning, combining automatic and robust human evaluations. |
| Outcome: | The proposed models generate relevant, coherent, and high-quality counterspeech, suggesting their suitability for efficient and responsible deployments. |
‘Aye’ or ‘No’? Speech-level Sentiment Analysis of Hansard UK Parliamentary Debate Transcripts (L18-1)
Copied to clipboard
| Challenge: | Transcripts of UK parliamentary debates are difficult for human readers to process due to the large quantity of textual data and the specialised language used. |
| Approach: | They propose to use annotated sentiment labels and labels derived from speakers' votes to classify the sentiment polarity of speakers as being either positive or negative towards motions proposed in the debates. |
| Outcome: | The proposed model outperforms existing models on a dataset of parliamentary debate transcripts using textual and contextual features. |
ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AI (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on abusive language towards conversational AI systems are not conclusive as they are not performed with live systems nor with real users due to the lack of reliable abuse detection tools. |
| Approach: | They propose to use a convAI dataset to account for the complexity of the task and to bench-mark existing models against this data. |
| Outcome: | The proposed model shows that abuse distribution is different compared to other datasets, with sexual tinted aggression towards the virtual persona of the systems. |
Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution (2024.acl-long)
Copied to clipboard
| Challenge: | Large language models reflect societal norms and biases, especially about gender. |
| Approach: | They propose to use large language models to examine gendered emotion attribution in five state-of-the-art LLMs to investigate whether emotions are genderes and whether they are influenced by societal stereotypes. |
| Outcome: | The proposed models exhibit gendered emotions, influenced by gender stereotypes, and the results are consistent with established research in psychology and gender studies. |
NLP for Counterspeech against Hate: A Survey and How-To Guide (2024.findings-naacl)
Copied to clipboard
| Challenge: | Recent studies have focused on the challenges of analysing, collecting, classifying, and automatically generating counterspeech, to reduce the huge burden of manually producing it. |
| Approach: | They propose a guide for doing research on counterspeech, with detailed examples and best practices that can be learnt from the NLP community. |
| Outcome: | The proposed strategies can reduce online and offline violence while preserving the freedom of speech of the users. |