Improved Semantic Role Labeling using Parameterized Neighborhood Memory Adaptation Article Swipe

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Ishan Jindal , Ranit Aharonov , Siddhartha Brahma , Huaiyu Zhu , Yunyao Li ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2011.14459

Deep neural models achieve some of the best results for semantic role labeling. Inspired by instance-based learning that utilizes nearest neighbors to handle low-frequency context-specific training samples, we investigate the use of memory adaptation techniques in deep neural models. We propose a parameterized neighborhood memory adaptive (PNMA) method that uses a parameterized representation of the nearest neighbors of tokens in a memory of activations and makes predictions based on the most similar samples in the training data. We empirically show that PNMA consistently improves the SRL performance of the base model irrespective of types of word embeddings. Coupled with contextualized word embeddings derived from BERT, PNMA improves over existing models for both span and dependency semantic parsing datasets, especially on out-of-domain text, reaching F1 scores of 80.2, and 84.97 on CoNLL2005, and CoNLL2009 datasets, respectively.

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Computer science Parameterized complexity Word (group theory) Artificial intelligence Representation (politics) Context (archaeology) Parsing Natural language processing Dependency grammar Domain (mathematical analysis) Artificial neural network Adaptation (eye) Machine learning Algorithm Mathematics Political science Paleontology Physics Geometry Optics Mathematical analysis Politics Biology Law

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2011.14459
PDF: https://arxiv.org/pdf/2011.14459
OA Status: green
Cited By: 4
References: 27
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2011.14459

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Title: Improved Semantic Role Labeling using Parameterized Neighborhood Memory Adaptation

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Language: en

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Publication year: 2020

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Publication date: 2020-11-29

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Authors: Ishan Jindal, Ranit Aharonov, Siddhartha Brahma, Huaiyu Zhu, Yunyao Li

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Landing page: https://arxiv.org/abs/2011.14459

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Concepts: Computer science, Parameterized complexity, Word (group theory), Artificial intelligence, Representation (politics), Context (archaeology), Parsing, Natural language processing, Dependency grammar, Domain (mathematical analysis), Artificial neural network, Adaptation (eye), Machine learning, Algorithm, Mathematics, Political science, Paleontology, Physics, Geometry, Optics, Mathematical analysis, Politics, Biology, Law

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Cited by: 4

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Citations by year (recent): 2023: 2, 2022: 1, 2021: 1

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abstract_inverted_index.learning	16
abstract_inverted_index.reaching	122
abstract_inverted_index.samples,	26
abstract_inverted_index.semantic	10, 115
abstract_inverted_index.training	25, 75
abstract_inverted_index.utilizes	18
abstract_inverted_index.CoNLL2009	132
abstract_inverted_index.datasets,	117, 133
abstract_inverted_index.labeling.	12
abstract_inverted_index.neighbors	20, 56
abstract_inverted_index.CoNLL2005,	130
abstract_inverted_index.adaptation	33
abstract_inverted_index.dependency	114
abstract_inverted_index.embeddings	101
abstract_inverted_index.especially	118
abstract_inverted_index.techniques	34
abstract_inverted_index.activations	63
abstract_inverted_index.embeddings.	96
abstract_inverted_index.empirically	78
abstract_inverted_index.investigate	28
abstract_inverted_index.performance	86
abstract_inverted_index.predictions	66
abstract_inverted_index.consistently	82
abstract_inverted_index.irrespective	91
abstract_inverted_index.neighborhood	43
abstract_inverted_index.low-frequency	23
abstract_inverted_index.out-of-domain	120
abstract_inverted_index.parameterized	42, 51
abstract_inverted_index.respectively.	134
abstract_inverted_index.contextualized	99
abstract_inverted_index.instance-based	15
abstract_inverted_index.representation	52
abstract_inverted_index.context-specific	24
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	5
citation_normalized_percentile