SapienzaNLP @ EACL 2021
2 papers at EACL!
This year at EACL 2021 our group will present 2 papers:
- Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration
- Us vs. Them: A Dataset of Populist Attitudes, News Bias and Emotions
Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration
by Simone Conia and Roberto Navigli
Recent studies treat Word Sense Disambiguation (WSD) as a single-label classification problem in which one is asked to choose only the best-fitting sense for a target word, given its context. However, gold data labelled by expert annotators suggest that maximizing the probability of a single sense may not be the most suitable training objective for WSD, especially if the sense inventory of choice is fine-grained. In this paper, we approach WSD as a multi-label classification problem in which multiple senses can be assigned to each target word. Not only does our simple method bear a closer resemblance to how human annotators disambiguate text, but it can also be seamlessly extended to exploit structured knowledge from semantic networks to achieve state-of-the-art results in English all-words WSD.
Us vs. Them: A Dataset of Populist Attitudes, News Bias and Emotions
by Pere-Lluís Huguet-Cabot, David Abadi, Agneta Fischer, Ekaterina Shutova
Computational modelling of political discourse tasks has become an increasingly important area of research in natural language processing. Populist rhetoric has risen across the political sphere in recent years; however, computational approaches to it have been scarce due to its complex nature. In this paper, we present the new Us vs. Them dataset, consisting of 6861 Reddit comments annotated for populist attitudes and the first large-scale computational models of this phenomenon. We investigate the relationship between populist mindsets and social groups, as well as a range of emotions typically associated with these. We set a baseline for two tasks related to populist attitudes and present a set of multi-task learning models that leverage and demonstrate the importance of emotion and group identification as auxiliary tasks.