Chinese couplet generation with neural network structures

Rui Yan, Cheng-Te Li, Xiaohua Hu, Ming Zhang

研究成果: Conference contribution

6 引文 (Scopus)

摘要

Part of the unique cultural heritage of China is the Chinese couplet. Given a sentence (namely an antecedent clause), people reply with another sentence (namely a subsequent clause) equal in length. Moreover, a special phenomenon is that corresponding characters from the same position in the two clauses match each other by following certain constraints on semantic and/or syntactic relatedness. Automatic couplet generation by computer is viewed as a difficult problem and has not been fully explored. In this paper, we formulate the task as a natural language generation problem using neural network structures. Given the issued antecedent clause, the system generates the subsequent clause via sequential language modeling. To satisfy special characteristics of couplets, we incorporate the attention mechanism and polishing schema into the encoding-decoding process. The couplet is generated incrementally and iteratively. A comprehensive evaluation, using perplexity and BLEU measurements as well as human judgments, has demonstrated the effectiveness of our proposed approach.

原文English
主出版物標題54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers
發行者Association for Computational Linguistics (ACL)
頁面2347-2357
頁數11
ISBN(電子)9781510827585
出版狀態Published - 2016 一月 1
事件54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Berlin, Germany
持續時間: 2016 八月 72016 八月 12

出版系列

名字54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers
4

Other

Other54th Annual Meeting of the Association for Computational Linguistics, ACL 2016
國家Germany
城市Berlin
期間16-08-0716-08-12

指紋

neural network
language
cultural heritage
semantics
China
evaluation
Couplet
Neural Networks
Clause

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Linguistics and Language

引用此文

Yan, R., Li, C-T., Hu, X., & Zhang, M. (2016). Chinese couplet generation with neural network structures. 於 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers (頁 2347-2357). (54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers; 卷 4). Association for Computational Linguistics (ACL).
Yan, Rui ; Li, Cheng-Te ; Hu, Xiaohua ; Zhang, Ming. / Chinese couplet generation with neural network structures. 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers. Association for Computational Linguistics (ACL), 2016. 頁 2347-2357 (54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers).
@inproceedings{23cc398825514806807254dfc536411b,
title = "Chinese couplet generation with neural network structures",
abstract = "Part of the unique cultural heritage of China is the Chinese couplet. Given a sentence (namely an antecedent clause), people reply with another sentence (namely a subsequent clause) equal in length. Moreover, a special phenomenon is that corresponding characters from the same position in the two clauses match each other by following certain constraints on semantic and/or syntactic relatedness. Automatic couplet generation by computer is viewed as a difficult problem and has not been fully explored. In this paper, we formulate the task as a natural language generation problem using neural network structures. Given the issued antecedent clause, the system generates the subsequent clause via sequential language modeling. To satisfy special characteristics of couplets, we incorporate the attention mechanism and polishing schema into the encoding-decoding process. The couplet is generated incrementally and iteratively. A comprehensive evaluation, using perplexity and BLEU measurements as well as human judgments, has demonstrated the effectiveness of our proposed approach.",
author = "Rui Yan and Cheng-Te Li and Xiaohua Hu and Ming Zhang",
year = "2016",
month = "1",
day = "1",
language = "English",
series = "54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers",
publisher = "Association for Computational Linguistics (ACL)",
pages = "2347--2357",
booktitle = "54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers",

}

Yan, R, Li, C-T, Hu, X & Zhang, M 2016, Chinese couplet generation with neural network structures. 於 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers. 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers, 卷 4, Association for Computational Linguistics (ACL), 頁 2347-2357, 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, Berlin, Germany, 16-08-07.

Chinese couplet generation with neural network structures. / Yan, Rui; Li, Cheng-Te; Hu, Xiaohua; Zhang, Ming.

54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers. Association for Computational Linguistics (ACL), 2016. p. 2347-2357 (54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers; 卷 4).

研究成果: Conference contribution

TY - GEN

T1 - Chinese couplet generation with neural network structures

AU - Yan, Rui

AU - Li, Cheng-Te

AU - Hu, Xiaohua

AU - Zhang, Ming

PY - 2016/1/1

Y1 - 2016/1/1

N2 - Part of the unique cultural heritage of China is the Chinese couplet. Given a sentence (namely an antecedent clause), people reply with another sentence (namely a subsequent clause) equal in length. Moreover, a special phenomenon is that corresponding characters from the same position in the two clauses match each other by following certain constraints on semantic and/or syntactic relatedness. Automatic couplet generation by computer is viewed as a difficult problem and has not been fully explored. In this paper, we formulate the task as a natural language generation problem using neural network structures. Given the issued antecedent clause, the system generates the subsequent clause via sequential language modeling. To satisfy special characteristics of couplets, we incorporate the attention mechanism and polishing schema into the encoding-decoding process. The couplet is generated incrementally and iteratively. A comprehensive evaluation, using perplexity and BLEU measurements as well as human judgments, has demonstrated the effectiveness of our proposed approach.

AB - Part of the unique cultural heritage of China is the Chinese couplet. Given a sentence (namely an antecedent clause), people reply with another sentence (namely a subsequent clause) equal in length. Moreover, a special phenomenon is that corresponding characters from the same position in the two clauses match each other by following certain constraints on semantic and/or syntactic relatedness. Automatic couplet generation by computer is viewed as a difficult problem and has not been fully explored. In this paper, we formulate the task as a natural language generation problem using neural network structures. Given the issued antecedent clause, the system generates the subsequent clause via sequential language modeling. To satisfy special characteristics of couplets, we incorporate the attention mechanism and polishing schema into the encoding-decoding process. The couplet is generated incrementally and iteratively. A comprehensive evaluation, using perplexity and BLEU measurements as well as human judgments, has demonstrated the effectiveness of our proposed approach.

UR - http://www.scopus.com/inward/record.url?scp=85011982262&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85011982262&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85011982262

T3 - 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers

SP - 2347

EP - 2357

BT - 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers

PB - Association for Computational Linguistics (ACL)

ER -

Yan R, Li C-T, Hu X, Zhang M. Chinese couplet generation with neural network structures. 於 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers. Association for Computational Linguistics (ACL). 2016. p. 2347-2357. (54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers).