Abstract
In natural language understanding, topics that touch upon figurative language and pragmatics are notably difficult. We probe a novel use of locally aggregated descriptors – specifically, an architecture called NeXtVLAD – motivated by its accomplishments in computer vision, achieve tremendous success in the FigLang2020 sarcasm detection task. The reported F1 score of 93.1% is 14% higher than the next best result. We specifically investigate the extent to which the novel architecture is responsible for this boost, and find that it does not provide statistically significant benefits. Deep learning approaches are expensive, and we hope our insights highlighting the lack of benefits from introducing a resource-intensive component will aid future research to distill the effective elements from long and complex pipelines, thereby providing a boost to the wider research community.- Anthology ID:
- 2021.insights-1.15
- Volume:
- Proceedings of the Second Workshop on Insights from Negative Results in NLP
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Editors:
- João Sedoc, Anna Rogers, Anna Rumshisky, Shabnam Tafreshi
- Venue:
- insights
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 103–109
- Language:
- URL:
- https://aclanthology.org/2021.insights-1.15
- DOI:
- 10.18653/v1/2021.insights-1.15
- Cite (ACL):
- Sina Mahdipour Saravani, Ritwik Banerjee, and Indrakshi Ray. 2021. An Investigation into the Contribution of Locally Aggregated Descriptors to Figurative Language Identification. In Proceedings of the Second Workshop on Insights from Negative Results in NLP, pages 103–109, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- An Investigation into the Contribution of Locally Aggregated Descriptors to Figurative Language Identification (Saravani et al., insights 2021)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2021.insights-1.15.pdf
- Code
- sinamps/nextvlad-for-nlp