Learning compositional structures for semantic graph parsing

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10440949" target="_blank" >RIV/00216208:11320/21:10440949 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Learning compositional structures for semantic graph parsing

Original language description

AM dependency parsing is a method for neural semantic graph parsing that exploits the principle of compositionality. While AM dependency parsers have been shown to be fast and accurate across several graphbanks, they require explicit annotations of the compositional tree structures for training. In the past, these were obtained using complex graphbank-specific heuristics written by experts. Here we show how they can instead be trained directly on the graphs with a neural latent-variable model, drastically reducing the amount and complexity of manual heuristics. We demonstrate that our model picks up on several linguistic phenomena on its own and achieves comparable accuracy to supervised training, greatly facilitating the use of AM dependency parsing for new sembanks.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

SPNLP 2021: THE 5TH WORKSHOP ON STRUCTURED PREDICTION FOR NLP

ISBN

978-1-954085-75-6

ISSN

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e-ISSN

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Number of pages

11

Pages from-to

22-32

Publisher name

ASSOC COMPUTATIONAL LINGUISTICS-ACL

Place of publication

STROUDSBURG

Event location

online

Event date

Aug 6, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000694721100003