Abstract
This paper describes multiple solutions designed and tested for the problem of word-level metaphor detection. The proposed systems are all based on variants of recurrent neural network architectures. Specifically, we explore multiple sources of information: pre-trained word embeddings (Glove), a dictionary of language concreteness and a transfer learning scenario based on the states of an encoder network from neural network machine translation system. One of the architectures is based on combining all three systems: (1) Neural CRF (Conditional Random Fields), trained directly on the metaphor data set; (2) Neural Machine Translation encoder of a transfer learning scenario; (3) a neural network used to predict final labels, trained directly on the metaphor data set. Our results vary between test sets: Neural CRF standalone is the best one on submission data, while combined system scores the highest on a test subset randomly selected from training data.- Anthology ID:
- W18-0917
- Volume:
- Proceedings of the Workshop on Figurative Language Processing
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Beata Beigman Klebanov, Ekaterina Shutova, Patricia Lichtenstein, Smaranda Muresan, Chee Wee
- Venue:
- Fig-Lang
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 128–132
- Language:
- URL:
- https://aclanthology.org/W18-0917
- DOI:
- 10.18653/v1/W18-0917
- Cite (ACL):
- Filip Skurniak, Maria Janicka, and Aleksander Wawer. 2018. Multi-Module Recurrent Neural Networks with Transfer Learning. In Proceedings of the Workshop on Figurative Language Processing, pages 128–132, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Multi-Module Recurrent Neural Networks with Transfer Learning (Skurniak et al., Fig-Lang 2018)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/W18-0917.pdf