@inproceedings{mathur-etal-2018-multi,
title = "Multi-lingual neural title generation for e-Commerce browse pages",
author = "Mathur, Prashant and
Ueffing, Nicola and
Leusch, Gregor",
editor = "Bangalore, Srinivas and
Chu-Carroll, Jennifer and
Li, Yunyao",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
month = jun,
year = "2018",
address = "New Orleans - Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-3020",
doi = "10.18653/v1/N18-3020",
pages = "162--169",
abstract = "To provide better access of the inventory to buyers and better search engine optimization, e-Commerce websites are automatically generating millions of browse pages. A browse page consists of a set of slot name/value pairs within a given category, grouping multiple items which share some characteristics. These browse pages require a title describing the content of the page. Since the number of browse pages are huge, manual creation of these titles is infeasible. Previous statistical and neural approaches depend heavily on the availability of large amounts of data in a language. In this research, we apply sequence-to-sequence models to generate titles for high-resource as well as low-resource languages by leveraging transfer learning. We train these models on multi-lingual data, thereby creating one joint model which can generate titles in various different languages. Performance of the title generation system is evaluated on three different languages; English, German, and French, with a particular focus on low-resourced French language.",
}
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<abstract>To provide better access of the inventory to buyers and better search engine optimization, e-Commerce websites are automatically generating millions of browse pages. A browse page consists of a set of slot name/value pairs within a given category, grouping multiple items which share some characteristics. These browse pages require a title describing the content of the page. Since the number of browse pages are huge, manual creation of these titles is infeasible. Previous statistical and neural approaches depend heavily on the availability of large amounts of data in a language. In this research, we apply sequence-to-sequence models to generate titles for high-resource as well as low-resource languages by leveraging transfer learning. We train these models on multi-lingual data, thereby creating one joint model which can generate titles in various different languages. Performance of the title generation system is evaluated on three different languages; English, German, and French, with a particular focus on low-resourced French language.</abstract>
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%0 Conference Proceedings
%T Multi-lingual neural title generation for e-Commerce browse pages
%A Mathur, Prashant
%A Ueffing, Nicola
%A Leusch, Gregor
%Y Bangalore, Srinivas
%Y Chu-Carroll, Jennifer
%Y Li, Yunyao
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans - Louisiana
%F mathur-etal-2018-multi
%X To provide better access of the inventory to buyers and better search engine optimization, e-Commerce websites are automatically generating millions of browse pages. A browse page consists of a set of slot name/value pairs within a given category, grouping multiple items which share some characteristics. These browse pages require a title describing the content of the page. Since the number of browse pages are huge, manual creation of these titles is infeasible. Previous statistical and neural approaches depend heavily on the availability of large amounts of data in a language. In this research, we apply sequence-to-sequence models to generate titles for high-resource as well as low-resource languages by leveraging transfer learning. We train these models on multi-lingual data, thereby creating one joint model which can generate titles in various different languages. Performance of the title generation system is evaluated on three different languages; English, German, and French, with a particular focus on low-resourced French language.
%R 10.18653/v1/N18-3020
%U https://aclanthology.org/N18-3020
%U https://doi.org/10.18653/v1/N18-3020
%P 162-169
Markdown (Informal)
[Multi-lingual neural title generation for e-Commerce browse pages](https://aclanthology.org/N18-3020) (Mathur et al., NAACL 2018)
ACL
- Prashant Mathur, Nicola Ueffing, and Gregor Leusch. 2018. Multi-lingual neural title generation for e-Commerce browse pages. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers), pages 162–169, New Orleans - Louisiana. Association for Computational Linguistics.