Abstract Meaning Representation 3.0 - Machine Translations

Item Name: Abstract Meaning Representation 3.0 - Machine Translations
Author(s): Bram Vanroy
LDC Catalog No.: LDC2024T11
ISLRN: 737-010-881-982-1
DOI: https://doi.org/10.35111/b94n-1y25
Release Date: December 16, 2024
Member Year(s): 2024
DCMI Type(s): Text
Data Source(s): broadcast conversation, discussion forum, newswire, web collection, weblogs
Project(s): ACE, BOLT, DEFT, GALE, LORELEI
Application(s): machine translation
Language(s): Dutch, Spanish, Irish
Language ID(s): nld, spa, gle
License(s): LDC User Agreement for Non-Members
Online Documentation: LDC2024T11 Documents
Licensing Instructions: Subscription & Standard Members, and Non-Members
Citation: Vanroy, Bram. Abstract Meaning Representation 3.0 - Machine Translations LDC2024T11. Web Download. Philadelphia: Linguistic Data Consortium, 2024.
Related Works: View

Introduction

Abstract Meaning Representation  3.0 - Machine Translations was developed by the Center for Computational Linguistics at KU Leuven in the HORIZON2020 project SignON. It is an automatic translation of a subset of sentences from Abstract Meaning Representation (AMR) Annotation Release 3.0 (LDC2020T02) into Spanish, Irish Gaelic, and Dutch.

AMR 3.0 is a semantic treebank of over 59,255 English natural language sentences from broadcast conversations, newswire, weblogs, web discussion forums, fiction and web text.

Data

The source sentences were drawn from material collected by the Linguistic Data Consortium, specifically, discussion forum text from the DARPA BOLT and DARPA DEFT programs, transcripts and English translations of Mandarin Chinese broadcast news programming, Wall Street Journal text, translated Xinhua news texts, various newswire texts from NIST OpenMT evaluations and weblog data from the DARPA GALE program.

AMR 3.0 training, development and test splits were translated into Spanish, Irish Gaelic, and Dutch using Google Translate. "Unsplit" directories were not translated and are not included in this release. Translations were not manually verified, but formal issues (such as unexpected new lines) were corrected, and special tokens and encoding issues were fixed with the Python tool ftfy.fix_text.

Data is presented in UTF-8 encoded txt files in PENMAN format.

Samples

Please view this text sample (TXT).

Updates

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