Zero-shot Transfer of Article-aware Legal Outcome Classification for European Court of Human Rights Cases

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A4B2M8CT5" target="_blank" >RIV/00216208:11320/23:4B2M8CT5 - isvavai.cz</a>

Výsledek na webu

<a href="https://aclanthology.org/2023.findings-eacl.44/" target="_blank" >https://aclanthology.org/2023.findings-eacl.44/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18653/v1/2023.findings-eacl.44" target="_blank" >10.18653/v1/2023.findings-eacl.44</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Zero-shot Transfer of Article-aware Legal Outcome Classification for European Court of Human Rights Cases

Popis výsledku v původním jazyce

"In this paper, we cast Legal Judgment Prediction on European Court of Human Rights cases into an article-aware classification task, where the case outcome is classified from a combined input of case facts and convention articles. This configuration facilitates the model learning some legal reasoning ability in mapping article text to specific case fact text. It also provides an opportunity to evaluate the model’s ability to generalize to zero-shot settings when asked to classify the case outcome with respect to articles not seen during training. We devise zero-shot experiments and apply domain adaptation methods based on domain discrimination and Wasserstein distance. Our results demonstrate that the article-aware architecture outperforms straightforward fact classification. We also find that domain adaptation methods improve zero-shot transfer performance, with article relatedness and encoder pre-training influencing the effect."

Název v anglickém jazyce

Zero-shot Transfer of Article-aware Legal Outcome Classification for European Court of Human Rights Cases

Popis výsledku anglicky

"In this paper, we cast Legal Judgment Prediction on European Court of Human Rights cases into an article-aware classification task, where the case outcome is classified from a combined input of case facts and convention articles. This configuration facilitates the model learning some legal reasoning ability in mapping article text to specific case fact text. It also provides an opportunity to evaluate the model’s ability to generalize to zero-shot settings when asked to classify the case outcome with respect to articles not seen during training. We devise zero-shot experiments and apply domain adaptation methods based on domain discrimination and Wasserstein distance. Our results demonstrate that the article-aware architecture outperforms straightforward fact classification. We also find that domain adaptation methods improve zero-shot transfer performance, with article relatedness and encoder pre-training influencing the effect."

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

—

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

"Findings of the Association for Computational Linguistics: EACL 2023"

ISBN

978-1-959429-47-0

ISSN

—

e-ISSN

—

Počet stran výsledku

13

Strana od-do

605-617

Název nakladatele

ACL

Místo vydání

Dubrovnik, Croatia

Místo konání akce

Dubrovnik, Croatia

Datum konání akce

1. 1. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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