Learning to Organize a Bag of Words into Sentences with Neural Networks: An Empirical Study
Chongyang Tao, Shen Gao, Juntao Li, Yansong Feng, Dongyan Zhao, Rui Yan
Abstract
Sequential information, a.k.a., orders, is assumed to be essential for processing a sequence with recurrent neural network or convolutional neural network based encoders. However, is it possible to encode natural languages without orders? Given a bag of words from a disordered sentence, humans may still be able to understand what those words mean by reordering or reconstructing them. Inspired by such an intuition, in this paper, we perform a study to investigate how “order” information takes effects in natural language learning. By running comprehensive comparisons, we quantitatively compare the ability of several representative neural models to organize sentences from a bag of words under three typical scenarios, and summarize some empirical findings and challenges, which can shed light on future research on this line of work.- Anthology ID:
- 2021.naacl-main.134
- Volume:
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1682–1691
- Language:
- URL:
- https://aclanthology.org/2021.naacl-main.134
- DOI:
- 10.18653/v1/2021.naacl-main.134
- Cite (ACL):
- Chongyang Tao, Shen Gao, Juntao Li, Yansong Feng, Dongyan Zhao, and Rui Yan. 2021. Learning to Organize a Bag of Words into Sentences with Neural Networks: An Empirical Study. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1682–1691, Online. Association for Computational Linguistics.
- Cite (Informal):
- Learning to Organize a Bag of Words into Sentences with Neural Networks: An Empirical Study (Tao et al., NAACL 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.naacl-main.134.pdf
- Data
- Penn Treebank