Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11779/1985
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Demir, Şeniz | - |
dc.date.accessioned | 2023-10-18T12:06:13Z | - |
dc.date.available | 2023-10-18T12:06:13Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Demir, S. (2023). Turkish Data-to-Text Generation Using Sequence-to-Sequence Neural Networks. ACM Transactions on Asian and Low-Resource Language Information Processing, 22(2), 1-27. | en_US |
dc.identifier.issn | 2375-4699 | - |
dc.identifier.issn | 2375-4702 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11779/1985 | - |
dc.identifier.uri | https://doi.org/10.1145/3543826 | - |
dc.description | TUBITAK-ARDEB [117E977] | en_US |
dc.description | This work is supported by TUBITAK-ARDEB under the grant number 117E977. | en_US |
dc.description.abstract | End-to-end data-driven approaches lead to rapid development of language generation and dialogue systems. Despite the need for large amounts of well-organized data, these approaches jointly learn multiple components of the traditional generation pipeline without requiring costly human intervention. End-to-end approaches also enable the use of loosely aligned parallel datasets in system development by relaxing the degree of semantic correspondences between training data representations and text spans. However, their potential in Turkish language generation has not yet been fully exploited. In this work, we apply sequenceto-sequence (Seq2Seq) neural models to Turkish data-to-text generation where the input data given in the form of a meaning representation is verbalized. We explore encoder-decoder architectures with attention mechanism in unidirectional, bidirectional, and stacked recurrent neural network (RNN) models. Our models generate one-sentence biographies and dining venue descriptions using a crowdsourced dataset where all field value pairs that appear in meaning representations are fully captured in reference sentences. To support this work, we also explore the performances of our models on a more challenging dataset, where the content of a meaning representation is too large to fit into a single sentence, and hence content selection and surface realization need to be learned jointly. This dataset is retrieved by coupling introductory sentences of person-related Turkish Wikipedia articles with their contained infobox tables. Our empirical experiments on both datasets demonstrate that Seq2Seq models are capable of generating coherent and fluent biographies and venue descriptions from field value pairs. We argue that the wealth of knowledge residing in our datasets and the insights obtained fromthis study hold the potential to give rise to the development of new end-to-end generation approaches for Turkish and other morphologically rich languages. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Assoc Computing Machinery | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Of-the-art | en_US |
dc.subject | Sequence-to-sequence model | en_US |
dc.subject | Turkish | en_US |
dc.subject | Wikipedia | en_US |
dc.subject | Natural-language generation | en_US |
dc.subject | Data-to-text generation | en_US |
dc.title | Turkish Data-To Generation Using Sequence-To Neural Networks | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1145/3543826 | - |
dc.identifier.scopus | 2-s2.0-85152906599 | en_US |
dc.description.PublishedMonth | Aralık | en_US |
dc.description.woscitationindex | Science Citation Index Expanded | - |
dc.identifier.wosquality | Q4 | - |
dc.description.WoSDocumentType | article | - |
dc.description.WoSInternationalCollaboration | Uluslararası işbirliği ile yapılmayan - HAYIR | en_US |
dc.description.WoSPublishedMonth | Nisan | en_US |
dc.description.WoSIndexDate | 2023 | en_US |
dc.description.WoSYOKperiod | YÖK - 2022-23 | en_US |
dc.identifier.scopusquality | Q2 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.issue | 2 | en_US |
dc.identifier.volume | 22 | en_US |
dc.department | Mühendislik Fakültesi, Elektrik Elektronik Mühendisligi Bölümü | en_US |
dc.relation.journal | Acm Transactions on Asian and Low-Resource Language Information Processing | en_US |
dc.identifier.wos | WOS:000963394900006 | en_US |
dc.institutionauthor | Demir, Şeniz | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.languageiso639-1 | en | - |
item.openairetype | Article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 02.02. Department of Computer Engineering | - |
Appears in Collections: | Elektrik Elektronik Mühendisliği Bölümü Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
CORE Recommender
SCOPUSTM
Citations
2
checked on Nov 16, 2024
WEB OF SCIENCETM
Citations
1
checked on Nov 16, 2024
Page view(s)
66
checked on Nov 18, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.