Adversarial Learning for Zero-Shot Stance Detection on Social Media
| Autor: | Malavika Srikanth, Emily Allaway, Kathleen R. McKeown |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language Computer science Shot (filmmaking) ComputerApplications_COMPUTERSINOTHERSYSTEMS 02 engineering and technology 010501 environmental sciences 01 natural sciences Test (assessment) Zero (linguistics) Adversarial system Human–computer interaction 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Social media Everyday life Computation and Language (cs.CL) 0105 earth and related environmental sciences Stance detection |
| Zdroj: | NAACL-HLT |
| Popis: | Stance detection on social media can help to identify and understand slanted news or commentary in everyday life. In this work, we propose a new model for zero-shot stance detection on Twitter that uses adversarial learning to generalize across topics. Our model achieves state-of-the-art performance on a number of unseen test topics with minimal computational costs. In addition, we extend zero-shot stance detection to new topics, highlighting future directions for zero-shot transfer. To appear in NAACL 2021 |
| Databáze: | OpenAIRE |
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