Can LSTM Learn to Capture Agreement? The Case of Basque

Shauli Ravfogel, Yoav Goldberg, Francis Tyers


Abstract
Sequential neural networks models are powerful tools in a variety of Natural Language Processing (NLP) tasks. The sequential nature of these models raises the questions: to what extent can these models implicitly learn hierarchical structures typical to human language, and what kind of grammatical phenomena can they acquire? We focus on the task of agreement prediction in Basque, as a case study for a task that requires implicit understanding of sentence structure and the acquisition of a complex but consistent morphological system. Analyzing experimental results from two syntactic prediction tasks – verb number prediction and suffix recovery – we find that sequential models perform worse on agreement prediction in Basque than one might expect on the basis of a previous agreement prediction work in English. Tentative findings based on diagnostic classifiers suggest the network makes use of local heuristics as a proxy for the hierarchical structure of the sentence. We propose the Basque agreement prediction task as challenging benchmark for models that attempt to learn regularities in human language.
Anthology ID:
W18-5412
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:
98–107
Language:
URL:
https://aclanthology.org/W18-5412
DOI:
10.18653/v1/W18-5412
Bibkey:
Cite (ACL):
Shauli Ravfogel, Yoav Goldberg, and Francis Tyers. 2018. Can LSTM Learn to Capture Agreement? The Case of Basque. In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pages 98–107, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Can LSTM Learn to Capture Agreement? The Case of Basque (Ravfogel et al., EMNLP 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-url/W18-5412.pdf