Linguistic Representations in Multi-task Neural Networks for Ellipsis Resolution

Abstract

Sluicing resolution is the task of identifying the antecedent to a question ellipsis. Antecedents are often sentential constituents, and previous work has therefore relied on syntactic parsing, together with complex linguistic features. A recent model instead used partial parsing as an auxiliary task in sequential neural network architectures to inject syntactic information. We explore the linguistic information being brought to bear by such networks, both by defining subsets of the data exhibiting relevant linguistic characteristics, and by examining the internal representations of the network. Both perspectives provide evidence for substantial linguistic knowledge being deployed by the neural networks.

BibTeX

@inproceedings{
 ronning2018linguistic,
 title={{Linguistic Representations in Multi-task Neural Networks for Ellipsis Resolution}},
 author={Ola R{\o }nning and Daniel Hardt and Anders S{\o }gaard},
 booktitle={Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP},
 pages={66--73},
 year={2018},
 address={Brussels, Belgium},
 publisher={Association for Computational Linguistics},
 doi={10.18653/v1/W18-5409},
 url={https://aclanthology.org/W18-5409/}
}