Abstract
Learning universal sentence representations which accurately model sentential semantic content is a current goal of natural language processing research. A prominent and successful approach is to train recurrent neural networks (RNNs) to encode sentences into fixed length vectors. Many core linguistic phenomena that one would like to model in universal sentence representations depend on syntactic structure. Despite the fact that RNNs do not have explicit syntactic structural representations, there is some evidence that RNNs can approximate such structure-dependent phenomena under certain conditions, in addition to their widespread success in practical tasks. In this work, we assess RNNs’ ability to learn the structure-dependent phenomenon of main clause tense.- Anthology ID:
- W18-5440
- Volume:
- Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP
- Month:
- November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 334–336
- Language:
- URL:
- https://aclanthology.org/W18-5440
- DOI:
- 10.18653/v1/W18-5440
- Cite (ACL):
- Geoff Bacon and Terry Regier. 2018. Probing sentence embeddings for structure-dependent tense. In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pages 334–336, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Probing sentence embeddings for structure-dependent tense (Bacon & Regier, EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W18-5440.pdf
- Data
- Universal Dependencies