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Memory Network for Linguistic Structure Parsing
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DOI：10.1109/TASLP.2020.3030500.png)
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Abstract
En 中文
Memory-based learning can be characterized as a lazy learning method in machine learning terminology because it delays the processing of input by storing the input until needed. Linguistic structure parsing, which has been in a performance improvement bottleneck since the latest series of works was presented, determines the syntactic or semantic structure of a sentence. In this article, we construct a memory component and use it to augment a linguistic structure parser which allows the parser to directly extract patterns from the known training treebank to form memory. The experimental results show that existing state-of-the-art parsers reach new heights of performance on the main benchmarks for dependency parsing and semantic role labeling with this memory network.
Keywords:
Semantics
Syntactics
Linguistics
Labeling
Random access memory
Task analysis
Speech processing
Memory network
semantic role labeling
syntactic dependency parsing
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