Home
Catalogue search
Refine your search:
Keyword
Creator / Publisher
Year
Medium
Type
BLLDB-Access:
free (3)
subject to license (0)
Search in the Catalogues and Directories
All fields
Title
Creator / Publisher
Keyword
Year
AND
OR
AND NOT
All fields
Title
Creator / Publisher
Keyword
Year
AND
OR
AND NOT
All fields
Title
Creator / Publisher
Keyword
Year
AND
OR
AND NOT
All fields
Title
Creator / Publisher
Keyword
Year
AND
OR
AND NOT
All fields
Title
Creator / Publisher
Keyword
Year
Sort by
creator [A → Z]
'
creator [Z → A]
'
publishing year ↑ (asc)
'
publishing year ↓ (desc)
'
title [A → Z]
'
title [Z → A]
'
Simple Search
Hits 1 – 3 of 3
1
Don't Go Far Off: An Empirical Study on Neural Poetry Translation ...
Chakrabarty, Tuhin
;
Saakyan, Arkadiy
;
Muresan, Smaranda
. - : arXiv, 2021
BASE
Show details
2
COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Saakyan, Arkadiy
. - : Underline Science Inc., 2021
BASE
Show details
3
Don't Go Far Off: An Empirical Study on Neural Poetry Translation ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Chakrabarty, Tuhin
;
Muresan, Smaranda
;
Saakyan, Arkadiy
. - : Underline Science Inc., 2021
Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.577/ Abstract: Despite constant improvements in machine translation quality, automatic poetry translation remains a challenging problem due to the lack of open-sourced parallel poetic corpora, and to the intrinsic complexities involved in preserving the semantics, style and figurative nature of poetry. We present an empirical investigation for poetry translation along several dimensions: 1) size and style of training data (poetic vs. non-poetic), including a zero-shot setup; 2) bilingual vs. multilingual learning; and 3) language-family-specific models vs. mixed-language-family models. To accomplish this, we contribute a parallel dataset of poetry translations for several language pairs. Our results show that multilingual fine-tuning on poetic text significantly outperforms multilingual fine-tuning on non-poetic text that is 35X larger in size, both in terms of automatic metrics (BLEU, BERTScore, COMET) and human evaluation metrics such as ...
Keyword:
Computational Linguistics
;
Language Models
;
Machine Learning
;
Machine Learning and Data Mining
;
Machine translation
;
Natural Language Processing
URL:
https://underline.io/lecture/37466-don't-go-far-off-an-empirical-study-on-neural-poetry-translation
https://dx.doi.org/10.48448/8zk8-5875
BASE
Hide details
Mobile view
All
Catalogues
UB Frankfurt Linguistik
0
IDS Mannheim
0
OLC Linguistik
0
UB Frankfurt Retrokatalog
0
DNB Subject Category Language
0
Institut für Empirische Sprachwissenschaft
0
Leibniz-Centre General Linguistics (ZAS)
0
Bibliographies
BLLDB
0
BDSL
0
IDS Bibliografie zur deutschen Grammatik
0
IDS Bibliografie zur Gesprächsforschung
0
IDS Konnektoren im Deutschen
0
IDS Präpositionen im Deutschen
0
IDS OBELEX meta
0
MPI-SHH Linguistics Collection
0
MPI for Psycholinguistics
0
Linked Open Data catalogues
Annohub
0
Online resources
Link directory
0
Journal directory
0
Database directory
0
Dictionary directory
0
Open access documents
BASE
3
Linguistik-Repository
0
IDS Publikationsserver
0
Online dissertations
0
Language Description Heritage
0
© 2013 - 2024 Lin|gu|is|tik
|
Imprint
|
Privacy Policy
|
Datenschutzeinstellungen ändern