@inproceedings{dda4363c3ec04ae585a846617f003f16,
title = "Translation of natural language query into keyword query using a rnn encoder-decoder",
abstract = "The number of natural language queries submi.ed to search engines is increasing as search environments get diversified. However, legacy search engines are still optimized for short keyword queries.Thus, the use of natural language queries at legacy search engines degrades the retrieval performance of the engines. This paper proposes a novel method to translate a natural language query into a keyword query relevant to the natural language query for retrieving beffer search results without change of the engines. .The proposed method formulates the translation as a generation task. .that is, the method generates a keyword query from a natural language query by preserving the semantics of the natural language query. A recurrent neural network encoder-decoder architecture is adopted as a generator of keyword queries from natural language queries. In addition, an a.ention mechanism is also used to cope with long natural language queries.",
keywords = "Neural machine translation, Query reformulation, RNN encoder-decoder, Translation natural language query into keyword query",
author = "Song, \{Hyun Je\} and Kim, \{A. Yeong\} and Park, \{Seong Bae\}",
note = "Publisher Copyright: {\textcopyright} 2017 Copyright held by the owner/author(s).; 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017 ; Conference date: 07-08-2017 Through 11-08-2017",
year = "2017",
month = aug,
day = "7",
doi = "10.1145/3077136.3080691",
language = "English",
series = "SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval",
publisher = "Association for Computing Machinery, Inc",
pages = "965--968",
booktitle = "SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval",
}