Beyond Emotion: A Multi-Modal Dataset for Human Desire Understanding
21346Authors
nan
Abstract
AbstractDesire is a strong wish to do or have something, which involves not only a linguistic expression, but also underlying cognitive phenomena driving human feelings. As the most primitive and basic human instinct, conscious desire is often accompanied by a range of emotional responses. As a strikingly understudied task, it is difficult for machines to model and understand desire due to the unavailability of benchmarking datasets with desire and emotion labels. To bridge this gap, we present MSED, the first multi-modal and multi-task sentiment, emotion and desire dataset, which contains 9,190 text-image pairs, with English text. Each multi-modal sample is annotated with six desires, three sentiments and six emotions. We also propose the state-of-the-art baselines to evaluate the potential of MSED and show the importance of multi-task and multi-modal clues for desire understanding. We hope this study provides a benchmark for human desire analysis. MSED will be publicly available for research.
Keywords
Full Content
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 127 - 157 July 10-15, 2022 ©2022 Association for Computational Linguistics NewsEdits: A News Article Revision Dataset and a Document-Level Reasoning Challenge Alexander Spangher1, Xiang Ren1, Jonathan May1, Nanyun Peng2 1Information Sciences Institute, University of Southern California 2Computer Science Department, University of California, Los Angeles {spangher, xiangren, jonmay}@usc.edu, violetpeng@cs.ucla.edu Abstract News article revision histories provide clues to narrative and factual evolution in news ar- ticles. To facilitate analysis of this evolution, we present the first publicly available dataset of news revision histories, NewsEdits. Our dataset is large-scale and multilingual; it contains 1.2 million articles with 4.6 million versions from over 22 English- and French-language newspa- per sources based in three countries, spanning 15 years of coverage (2006-2021).1 We define article-level edit actions: Addition, Deletion, Edit and Refactor, and develop a high- accuracy extraction algorithm to identify these actions. To underscore the factual nature of many edit actions, we conduct analyses show- ing that added and deleted sentences are more likely to contain updating events, main content and quotes than unchanged sentences. Finally, to explore whether edit actions are pre- dictable, we introduce three novel tasks aimed at predicting actions performed during version updates. We show that these tasks are possible for expert humans but are challenging for large NLP models. We hope this can spur research in narrative framing and help provide predictive tools for journalists chasing breaking news. 1 Introduction Revision histories gathered from various natural language domains like Wikipedia (Grundkiewicz and Junczys-Dowmunt, 2014), Wikihow (Faruqui et al., 2018) and student learner essays (Zhang and Litman, 2015) have primarily been studied to ex- plore stylistic changes, such as grammatical error correction (Shah et al., 2020) and argumentation design (Afrin et al., 2020). However, deeper ques- tions about content updates and narrative evolution are underexplored: Which facts are uncertain and likely to be changed? Which events are likely to 1We release the dataset and all code used in modeling and evaluation: https://github.com/isi-nlp/NewsEdits. git Figure 1: We identify sentence-level operations – Edit, Addition, Deletion and Refactor – between two versions of a news article (merges, shown here, and splits are a special cases of Edits). We propose tasks aimed at predicting these operations on article versions. We char- acterize aspects of additions, deletions and edits. We hope NewsEdits can contribute to research on narrative and factual development patterns. update? What voices and perspectives are needed to complete a narrative? Existing edits corpora do not address these ques- tions due to the nature of previously studied do- mains: as shown in Yang et al. (2017), the distribu- tion of edits in other domains, like Wikipedia, tend to focus on syntax or style edits. In this work, we in- troduce a novel domain for revision histories, news article revision histories which, we show, covers the updating of events. Many edits in news either (1) incorporate new information (2) update events or (3) broaden …
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Labeled Text
AbstractDesire is a strong wish to do or have something, which involves not only a linguistic expression, but also underlying cognitive phenomena driving human feelings. As the most primitive and basic human instinct, conscious desire is often accompanied by a range of emotional responses. As a strikingly understudied task, it is difficult for machines to model and understand desire due to the unavailability of benchmarking datasets with desire and emotion labels. To bridge this gap, we present MSED, the first multi-modal and multi-task sentiment, emotion and desire dataset, which contains 9,190 text-image pairs, with English text. Each multi-modal sample is annotated with six desires, three sentiments and six emotions. We also propose the state-of-the-art baselines to evaluate the potential of MSED and show the importance of multi-task and multi-modal clues for desire understanding. We hope this study provides a benchmark for human desire analysis. MSED will be publicly available for research.
Publication Details
- Paper ID:
- 21346
- Year:
- 2022
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan