@inproceedings{boschee-etal-2019-saral,
title = "{SARAL}: A Low-Resource Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage",
author = "Boschee, Elizabeth and
Barry, Joel and
Billa, Jayadev and
Freedman, Marjorie and
Gowda, Thamme and
Lignos, Constantine and
Palen-Michel, Chester and
Pust, Michael and
Khonglah, Banriskhem Kayang and
Madikeri, Srikanth and
May, Jonathan and
Miller, Scott",
editor = "Costa-juss{\`a}, Marta R. and
Alfonseca, Enrique",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-3004",
doi = "10.18653/v1/P19-3004",
pages = "19--24",
abstract = "With the increasing democratization of electronic media, vast information resources are available in less-frequently-taught languages such as Swahili or Somali. That information, which may be crucially important and not available elsewhere, can be difficult for monolingual English speakers to effectively access. In this paper we present an end-to-end cross-lingual information retrieval (CLIR) and summarization system for low-resource languages that 1) enables English speakers to search foreign language repositories of text and audio using English queries, 2) summarizes the retrieved documents in English with respect to a particular information need, and 3) provides complete transcriptions and translations as needed. The SARAL system achieved the top end-to-end performance in the most recent IARPA MATERIAL CLIR+summarization evaluations. Our demonstration system provides end-to-end open query retrieval and summarization capability, and presents the original source text or audio, speech transcription, and machine translation, for two low resource languages.",
}
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%0 Conference Proceedings
%T SARAL: A Low-Resource Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage
%A Boschee, Elizabeth
%A Barry, Joel
%A Billa, Jayadev
%A Freedman, Marjorie
%A Gowda, Thamme
%A Lignos, Constantine
%A Palen-Michel, Chester
%A Pust, Michael
%A Khonglah, Banriskhem Kayang
%A Madikeri, Srikanth
%A May, Jonathan
%A Miller, Scott
%Y Costa-jussà, Marta R.
%Y Alfonseca, Enrique
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F boschee-etal-2019-saral
%X With the increasing democratization of electronic media, vast information resources are available in less-frequently-taught languages such as Swahili or Somali. That information, which may be crucially important and not available elsewhere, can be difficult for monolingual English speakers to effectively access. In this paper we present an end-to-end cross-lingual information retrieval (CLIR) and summarization system for low-resource languages that 1) enables English speakers to search foreign language repositories of text and audio using English queries, 2) summarizes the retrieved documents in English with respect to a particular information need, and 3) provides complete transcriptions and translations as needed. The SARAL system achieved the top end-to-end performance in the most recent IARPA MATERIAL CLIR+summarization evaluations. Our demonstration system provides end-to-end open query retrieval and summarization capability, and presents the original source text or audio, speech transcription, and machine translation, for two low resource languages.
%R 10.18653/v1/P19-3004
%U https://aclanthology.org/P19-3004
%U https://doi.org/10.18653/v1/P19-3004
%P 19-24
Markdown (Informal)
[SARAL: A Low-Resource Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage](https://aclanthology.org/P19-3004) (Boschee et al., ACL 2019)
ACL
- Elizabeth Boschee, Joel Barry, Jayadev Billa, Marjorie Freedman, Thamme Gowda, Constantine Lignos, Chester Palen-Michel, Michael Pust, Banriskhem Kayang Khonglah, Srikanth Madikeri, Jonathan May, and Scott Miller. 2019. SARAL: A Low-Resource Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 19–24, Florence, Italy. Association for Computational Linguistics.