|
|
Generating Sentences of Contrast Relationship |
Jiao Qihang,Le Xiaoqiu() |
National Science Library, Chinese Academy of Sciences, Beijing 100190, China Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China |
|
|
Abstract [Objective] This paper tries to generate contrastive sentences from two related paragraphs, aiming to establish a new model for creating contrastive paragraphs. [Methods] We generated contrastive sentences automatically from contrastive text sequences. We designed a deep learning model based on Seq2seq, which incorporated contrast features with character vectors to represent texts. Both the Encoder and Decoder layers of our model used BiLSTM structure, which also included attention mechanism. [Results] We examined the proposed model with manually annotated search lists and scientific papers. Then, we adopted BLEU as evaluation index for the results. The final evaluation score was 12.1, which was 6.5 higher than those of the benchmark model using BiLSTM + Attention. [Limitations] Due to the complexity of manually labeling, the data size in our experiments was small. [Conclusions] The proposed model could be used to build new model for generating contrastive paragraphs.
|
Received: 10 December 2019
Published: 07 July 2020
|
|
Corresponding Authors:
Le Xiaoqiu
E-mail: lexq@mail.las.ac.cn
|
[1] |
万小军, 冯岩松, 孙薇薇. 文本自动生成研究进展与趋势[R]. 北京:北京大学, 2016: 1-2.
|
[1] |
( Wan Xiaojun, Feng Yansong, Sun Weiwei. Research Progress and Trend of Automatic Text Generation[R]. Beijing: Peking University, 2016: 1-2.)
|
[2] |
Mihalcea R, Tarau P. TextRank: Bringing Order into Text [C]//Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing. 2004: 404-411.
|
[3] |
林汝昌, 李曼珏. 语义的对比关系和对立关系[J]. 外语教学与研究, 1987(2):15-21.
|
[3] |
( Lin Ruchang, Li Manjue. On Semantic Opposites and Contrasts[[J]. Foreign Language Teaching and Research, 1987(2):15-21.)
|
[4] |
车竞. 现代汉语比较句论略[J]. 湖北师范学院学报:哲学社会科学版, 2005,25(3):60-63.
|
[4] |
( Che Jing. A Brief Analysis of Comparative Sentences in Modern Chinese[J]. Journal of Hubei Normal University:Philosophy and Social Sciences, 2005,25(3):60-63.)
|
[5] |
魏阳阳. 现代汉语三种平比句型的语义认知机制研究[J]. 理论月刊, 2017(12):75-80.
|
[5] |
( Wei Yangyang. A Study on the Semantic Cognitive Mechanism of Three Parable Sentence Patterns in Modern Chinese[[J]. Theory Monthly, 2017(12):75-80.)
|
[6] |
Jindal N, Liu B. Identifying Comparative Sentences in Text Documents [C]//Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2006: 244-251.
|
[7] |
黄小江, 万小军, 杨建武, 等. 汉语比较句识别研究[J]. 中文信息学报, 2008,22(5):30-38.
|
[7] |
( Huang Xiaojiang, Wan Xiaojun, Yang Jianwu, et al. Learning to Identify Chinese Comparative Sentences[J]. Journal of Chinese Information Processing, 2008,22(5):30-38.)
|
[8] |
白林楠, 胡韧奋, 刘智颖. 基于句法语义规则系统的比较句自动识别[J]. 北京大学学报(自然科学版), 2015,51(2):275-281.
|
[8] |
( Bai Linnan, Hu Renfen, Liu Zhiying. Recognition of Comparative Sentences Based on Syntactic and Semantic Rules-System[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2015,51(2):275-281.)
|
[9] |
吴晨, 韦向峰. 用户评价中比较句的识别和倾向性分析[J]. 计算机科学, 2016,43(S1):435-439.
|
[9] |
( Wu Chen, Wei Xiangfeng. Opinion Analysis and Recognition of Comparative Sentences in User Views[J]. Computer Science, 2016,43(S1):435-439.)
|
[10] |
朱茂然, 王奕磊, 高松, 等. 中文比较关系的识别: 基于注意力机制的深度学习模型[J]. 情报学报, 2019,38(6):612-621.
|
[10] |
( Zhu Maoran, Wang Yilei, Gao Song, el at. A Deep-Learning Model Based on Attention Mechanism for Chinese Comparative Relation Detection[J]. Journal of the China Society for Scientific and Technical Information, 2019,38(6):612-621.)
|
[11] |
Baxendale P B. Machine-made Index for Technical Literature—An Experiment[J]. IBM Journal of Research and Development, 1958,2(4):354-361.
doi: 10.1147/rd.24.0354
|
[12] |
Edmundson H P. New Methods in Automatic Extracting[J]. Journal of the ACM, 1969,16(2):264-285.
doi: 10.1145/321510.321519
|
[13] |
Gkatzia D, Lemon O, Rieser V. Natural Language Generation Enhances Human Decision-making with Uncertain Information[OL]. arXiv Preprint, arXiv: 1606. 03254.
|
[14] |
Lopez A. Statistical Machine Translation[J]. ACM Computing Surveys, 2008,40(3). DOI: 10.1145/1380584.1380586.
|
[15] |
Sutskever I, Vinyals O, Le Q V. Sequence to Sequence Learning with Neural Networks[OL]. arXiv Preprint, arXiv: 1409. 3215.
|
[16] |
Shi T, Keneshloo Y, Ramakrishnan N, et al. Neural Abstractive Text Summarization with Sequence-to-Sequence Models : A Survey [OL]. arXiv Preprint, arXiv: 1812. 02303.
|
[17] |
Jain P, Agrawal P, Mishra A, et al. Story Generation from Sequence of Independent Short Descriptions[OL]. arXiv Preprint, arXiv: 1707. 05501.
|
[18] |
Liu T, Wang K, Sha L, et al. Table-to-Text Generation by Structure-aware Seq2Seq Learning [C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence. 2018.
|
[19] |
Deng Y, Kim Y, Chiu J, et al. Latent Alignment and Variational Attention [C]//Advances in Neural Information Processing Systems. 2018: 9712-9724.
|
[20] |
Li J, Monroe W, Shi T, et al. Adversarial Learning for Neural Dialogue Generation[OL]. arXiv Preprint, arXiv: 1701. 06547.
|
[21] |
Al-Rfou R, Perozzi B, Skiena S. Polyglot: Distributed Word Representations for Multilingual NLP[OL]. arXiv Preprint, arXiv: 1307. 1662.
|
[22] |
Papineni K, Roukos S, Ward T, et al. BLEU: A Method for Automatic Evaluation of Machine Translation [C]//Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2002: 311-318.
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|