Myrthe Reuver


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Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Study
Myrthe Reuver | Suzan Verberne | Antske Fokkens
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

For a viewpoint-diverse news recommender, identifying whether two news articles express the same viewpoint is essential. One way to determine “same or different” viewpoint is stance detection. In this paper, we investigate the robustness of operationalization choices for few-shot stance detection, with special attention to modelling stance across different topics. Our experiments test pre-registered hypotheses on stance detection. Specifically, we compare two stance task definitions (Pro/Con versus Same Side Stance), two LLM architectures (bi-encoding versus cross-encoding), and adding Natural Language Inference knowledge, with pre-trained RoBERTa models trained with shots of 100 examples from 7 different stance detection datasets. Some of our hypotheses and claims from earlier work can be confirmed, while others give more inconsistent results. The effect of the Same Side Stance definition on performance differs per dataset and is influenced by other modelling choices. We found no relationship between the number of training topics in the training shots and performance. In general, cross-encoding out-performs bi-encoding, and adding NLI training to our models gives considerable improvement, but these results are not consistent across all datasets. Our results indicate that it is essential to include multiple datasets and systematic modelling experiments when aiming to find robust modelling choices for the concept ‘stance’.


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Will It Blend? Mixing Training Paradigms & Prompting for Argument Quality Prediction
Michiel van der Meer | Myrthe Reuver | Urja Khurana | Lea Krause | Selene Baez Santamaria
Proceedings of the 9th Workshop on Argument Mining

This paper describes our contributions to the Shared Task of the 9th Workshop on Argument Mining (2022). Our approach uses Large Language Models for the task of Argument Quality Prediction. We perform prompt engineering using GPT-3, and also investigate the training paradigms multi-task learning, contrastive learning, and intermediate-task training. We find that a mixed prediction setup outperforms single models. Prompting GPT-3 works best for predicting argument validity, and argument novelty is best estimated by a model trained using all three training paradigms.


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Is Stance Detection Topic-Independent and Cross-topic Generalizable? - A Reproduction Study
Myrthe Reuver | Suzan Verberne | Roser Morante | Antske Fokkens
Proceedings of the 8th Workshop on Argument Mining

Cross-topic stance detection is the task to automatically detect stances (pro, against, or neutral) on unseen topics. We successfully reproduce state-of-the-art cross-topic stance detection work (Reimers et. al, 2019), and systematically analyze its reproducibility. Our attention then turns to the cross-topic aspect of this work, and the specificity of topics in terms of vocabulary and socio-cultural context. We ask: To what extent is stance detection topic-independent and generalizable across topics? We compare the model’s performance on various unseen topics, and find topic (e.g. abortion, cloning), class (e.g. pro, con), and their interaction affecting the model’s performance. We conclude that investigating performance on different topics, and addressing topic-specific vocabulary and context, is a future avenue for cross-topic stance detection. References Nils Reimers, Benjamin Schiller, Tilman Beck, Johannes Daxenberger, Christian Stab, and Iryna Gurevych. 2019. Classification and Clustering of Arguments with Contextualized Word Embeddings. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 567–578, Florence, Italy. Association for Computational Linguistics.

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No NLP Task Should be an Island: Multi-disciplinarity for Diversity in News Recommender Systems
Myrthe Reuver | Antske Fokkens | Suzan Verberne
Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation

Natural Language Processing (NLP) is defined by specific, separate tasks, with each their own literature, benchmark datasets, and definitions. In this position paper, we argue that for a complex problem such as the threat to democracy by non-diverse news recommender systems, it is important to take into account a higher-order, normative goal and its implications. Experts in ethics, political science and media studies have suggested that news recommendation systems could be used to support a deliberative democracy. We reflect on the role of NLP in recommendation systems with this specific goal in mind and show that this theory of democracy helps to identify which NLP tasks and techniques can support this goal, and what work still needs to be done. This leads to recommendations for NLP researchers working on this specific problem as well as researchers working on other complex multidisciplinary problems.

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Implementing Evaluation Metrics Based on Theories of Democracy in News Comment Recommendation (Hackathon Report)
Myrthe Reuver | Nicolas Mattis
Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation

Diversity in news recommendation is important for democratic debate. Current recommendation strategies, as well as evaluation metrics for recommender systems, do not explicitly focus on this aspect of news recommendation. In the 2021 Embeddia Hackathon, we implemented one novel, normative theory-based evaluation metric, “activation”, and use it to compare two recommendation strategies of New York Times comments, one based on user likes and another on editor picks. We found that both comment recommendation strategies lead to recommendations consistently less activating than the available comments in the pool of data, but the editor’s picks more so. This might indicate that New York Times editors’ support a deliberative democratic model, in which less activation is deemed ideal for democratic debate.

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Are we human, or are we users? The role of natural language processing in human-centric news recommenders that nudge users to diverse content
Myrthe Reuver | Nicolas Mattis | Marijn Sax | Suzan Verberne | Nava Tintarev | Natali Helberger | Judith Moeller | Sanne Vrijenhoek | Antske Fokkens | Wouter van Atteveldt
Proceedings of the 1st Workshop on NLP for Positive Impact

In this position paper, we present a research agenda and ideas for facilitating exposure to diverse viewpoints in news recommendation. Recommending news from diverse viewpoints is important to prevent potential filter bubble effects in news consumption, and stimulate a healthy democratic debate. To account for the complexity that is inherent to humans as citizens in a democracy, we anticipate (among others) individual-level differences in acceptance of diversity. We connect this idea to techniques in Natural Language Processing, where distributional language models would allow us to place different users and news articles in a multidimensional space based on semantic content, where diversity is operationalized as distance and variance. In this way, we can model individual “latitudes of diversity” for different users, and thus personalize viewpoint diversity in support of a healthy public debate. In addition, we identify technical, ethical and conceptual issues related to our presented ideas. Our investigation describes how NLP can play a central role in diversifying news recommendations.