A Semi-Autoregressive Graph Generative Model for Dependency Graph Parsing

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A3Y9GLXZF" target="_blank" >RIV/00216208:11320/23:3Y9GLXZF - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175453608&partnerID=40&md5=48eafa31bcd4e1a9c15b3ed75e7a014e" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175453608&partnerID=40&md5=48eafa31bcd4e1a9c15b3ed75e7a014e</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

A Semi-Autoregressive Graph Generative Model for Dependency Graph Parsing

Original language description

"Recent years have witnessed the impressive progress in Neural Dependency Parsing. According to the different factorization approaches to the graph joint probabilities, existing parsers can be roughly divided into autoregressive and non-autoregressive patterns. The former means that the graph should be factorized into multiple sequentially dependent components, then it can be built up component by component. And the latter assumes these components to be independent so that they can be outputted in a one-shot manner. However, when treating the directed edge as an explicit dependency relationship, we discover that there is a mixture of independent and interdependent components in the dependency graph, signifying that both aforementioned models fail to precisely capture the explicit dependencies among nodes and edges. Based on this property, we design a Semi-Autoregressive Dependency Parser to generate dependency graphs via adding node groups and edge groups autoregressively while pouring out all group elements in parallel. The model gains a trade-off between non-autoregression and autoregression, which respectively suffer from the lack of target inter-dependencies and the uncertainty of graph generation orders. The experiments show the proposed parser outperforms strong baselines on Enhanced Universal Dependencies of multiple languages, especially achieving 4% average promotion at graph-level accuracy. Also, the performances of model variations show the importance of specific parts. © 2023 Association for Computational Linguistics."

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

2023

Confidentiality

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

Article name in the collection

"Proc. Annu. Meet. Assoc. Comput Linguist."

ISBN

978-195942962-3

ISSN

0736-587X

e-ISSN

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Number of pages

13

Pages from-to

4218-4230

Publisher name

Association for Computational Linguistics (ACL)

Place of publication

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Event location

Cham

Event date

Jan 1, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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