research-article

Enhancing Coherence and Diversity in Multi-class Slogan Generation Systems

Authors:

Pir Noman Ahmad,

Mohammad ShabazAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 23, Issue 8

Article No.: 120, Pages 1 - 24

https://doi.org/10.1145/3637551

Published: 07 August 2024 Publication History

Abstract

Many problems related to natural language processing are solved by neural networks and big data. Researchers have previously focused on single-task supervised goals with limited data management to train slogan classification. A multi-task learning framework is used to learn jointly across several tasks related to generating multi-class slogan types. This study proposes a multi-task model named slogan generative adversarial network systems (Slo-GAN) to enhance coherence and diversity in slogan generation, utilizing generative adversarial networks and recurrent neural networks (RNN). Slo-GAN generates a new text slogan-type corpus, and the training generalization process is improved. We explored active learning (AL) and meta-learning (ML) for dataset labeling efficiency. AL reduced annotations by 10% compared to ML but still needed about 70% of the full dataset for baseline performance. The whole framework of Slo-GAN is supervised and trained together on all of these tasks. The text with the higher reporting score level is filtered by Slo-GAN, and a classification accuracy of 87.2% is achieved. We leveraged relevant datasets to perform a cross-domain experiment, reinforcing our assertions regarding both the distinctiveness of our dataset and the challenges of adapting bilingual dialects to one another.

References

[1]

Somayeh Abdi and Abdollah Irandoust. 2013. The importance of advertising slogans and their proper designing in brand equity. Int. J. Organiz. Leadersh. 2, 2 (2013), 62–69.

[2]

Pir Noman Ahmad, Yuanchao Liu, Gauhar Ali, Mudasir Ahmad Wani, and Mohammed ElAffendi. 2023. Robust benchmark for propagandist text detection and mining high-quality data. Mathematics 11, 12 (2023), 2668. DOI:

[3]

Pir Noman Ahmad, Yuanchao Liu, Khalid Khan, Tao Jiang, and Umama Burhan. 2023. BIR: Biomedical information retrieval system for cancer treatment in electronic health record using transformers. Sensors 23, 23 (2023), 9355.

[4]

Abdullah Saleh Alqahtani, P. Saravanan, M. Maheswari, and Sami Alshmrany. 2022. An automatic query expansion based on hybrid CMO-COOT algorithm for optimized information retrieval. J. Supercomput. 78, 6 (2022), 8625–8643. DOI:

Digital Library

[5]

Shizhe Diao, Xinwei Shen, Kashun Shum, Yan Song, and Tong Zhang. 2021. TILGAN: Transformer-based implicit latent GAN for diverse and coherent text generation. In Findings of the Association for Computational Linguistics (ACL-IJCNLP’21) (2021). 4844–4858.

[6]

Mostaq Ahmed, Partha Chakraborty, and Tanupriya Choudhury. 2022. Bangla document categorization using deep RNN model with attention mechanism. In Cyber Intelligence and Information Retrieval. Springer, 137–147.

[7]

Huma Akhtar and Hamna Khalid. 2019. The use of English words in Urdu magazine print advertisements: An analysis. Bahria Univ. J. Humanit. Soc. Sci. 2, 1 (2019), 14–14.

[8]

Khalid Alnajjar, Hadaytullah Kundi, and Hannu Toivonen. 2018. “ Talent, Skill and Support.”: A method for automatic creation of slogans. In Proceedings of the 9th International Conference on Computational Creativity. Association for Computational Creativity.

[9]

Khalid Alnajjar and Hannu Toivonen. 2021. Computational generation of slogans. Nat. Lang. Eng. 27, 5 (2021), 575–607.

[10]

Pir Noman Ahmad, Adnan Muhammad Shah, and KangYoon Lee. 2023. Propaganda detection in public Covid-19 discussion on social media. Proceeding of the (PACIS'23), 193. https://aisel.aisnet.org/pacis2023/193

[11]

Margaret M. Bradley and Peter J. Lang. 1999. Affective Norms for English words (ANEW): Instruction Manual and Affective Ratings. Technical report C-1. The Center for Research in Psychophysiology.

[12]

Xavier A. Carrasco, Ashraf Elnagar, and Mohammed Lataifeh. 2021. A generative adversarial network for data augmentation: The case of Arabic regional dialects. Procedia Comput. Sci. 189, (2021), 92–99.

[13]

Junfan Chen, Richong Zhang, Yongyi Mao, and Jie Xu. 2022. ContrastNet: A contrastive learning framework for few-shot text classification. In Proceedings of the AAAI Conference on Artificial Intelligence. 10492–10500.

[14]

WenShin Chen and Rudy Hirschheim. 2004. A paradigmatic and methodological examination of information systems research from 1991 to 2001. Inf. Syst. J. 14, 3 (2004), 197–235.

[15]

Yizhou Chen, Xu-Hua Yang, Zihan Wei, Ali Asghar Heidari, Nenggan Zheng, Zhicheng Li, Huiling Chen, Haigen Hu, Qianwei Zhou, and Qiu Guan. 2022. Generative adversarial networks in medical image augmentation: A review. Comput. Biol. Med. 144, 1 (2022), 105382.

[16]

Yu-Fu Chen and Szu-Hao Huang. 2021. Sentiment-influenced trading system based on multimodal deep reinforcement learning. Appl. Soft Comput. 112, (2021), 107788.

[17]

Kyunghyun Cho, Aaron Courville, and Yoshua Bengio. 2015. Describing multimedia content using attention-based encoder-decoder networks. IEEE Trans. Multim. 17, 11 (2015), 1875–1886.

Digital Library

[18]

Francesca M. M. Citron, Brendan S. Weekes, and Evelyn C. Ferstl. 2014. How are affective word ratings related to lexicosemantic properties? Evidence from the Sussex Affective Word List. Appl. Psycholing. 35, 2 (2014), 313–331.

[19]

Giovanni Da San Martino, Alberto Barrón-Cedeño, and Preslav Nakov. 2019. Findings of the NLP4IF-2019 shared task on fine-grained propaganda detection. In Proceedings of the 2nd Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda. Association for Computational Linguistics, 162–170. DOI:

[20]

Mayukh Dass, Chiranjeev Kohli, and Manaswini Acharya. 2023. An Investigation into slogan design on creating slogan–brand alignment: Message clarity and creativity enhance while jingles and rhymes weaken alignment. J. Advert. Res. 63, 1 (2023), 43–60.

[21]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, 4171–4186. DOI:

[22]

Pir Noman Ahmad, Adnan Muhammad Shah, and KangYoon Lee. 2023. A review on electronic health record text-mining for biomedical name entity recognition in healthcare domain. In Proceedings of the Healthcare Conference. MDPI, 1268.

[23]

Tom Dobber, Nadia Metoui, Damian Trilling, Natali Helberger, and Claes de Vreese. 2021. Do (microtargeted) deepfakes have real effects on political attitudes? Int. J. Press/Polit. 26, 1 (2021), 69–91.

[24]

Mateusz Dolata, Stefan Feuerriegel, and Gerhard Schwabe. 2022. A sociotechnical view of algorithmic fairness. Inf. Syst. J. 32, 4 (2022), 754–818.

[25]

Grahame R. Dowling and Boris Kabanoff. 1996. Computer-aided content analysis: What do 240 advertising slogans have in common? Market. Lett. 7, 1 (1996), 63–75.

[26]

Tatjana Dubovičienė and Pavel Skorupa. 2014. The analysis of some stylistic features of English advertising slogans. Man the Word/Zmogus ir zodis 16, 3 (2014).

[27]

Nima Ebadi, Mohsen Jozani, Kim-Kwang Raymond Choo, and Paul Rad. 2021. A memory network information retrieval model for identification of news misinformation. IEEE Trans. Big Data 8, 5 (2021), 1358--1370.

[28]

Ashraf Elnagar, Ridhwan Al-Debsi, and Omar Einea. 2020. Arabic text classification using deep learning models. Inf. Process. Manag. 57, 1 (2020), 102121.

Digital Library

[29]

Mauajama Firdaus, Naveen Thangavelu, Asif Ekbal, and Pushpak Bhattacharyya. 2022. I enjoy writing and playing, do you: A personalized and emotion grounded dialogue agent using generative adversarial network. IEEE Trans. Affect. Comput. 14, 3 (2022), 2127–2138.

Digital Library

[30]

Nuria Galí, Raquel Camprubí, and José A. Donaire. 2017. Analysing tourism slogans in top tourism destinations. J. Destin. Market. Manag. 6, 3 (2017), 243–251.

[31]

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. Adv. Neural Inf. Process. Syst. 27, (2014).

[32]

Alex Graves and Jürgen Schmidhuber. 2005. Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 18, 5–6 (2005), 602–610.

Digital Library

[33]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computat. 9, 8 (1997), 1735–1780.

Digital Library

[34]

Lindsay Pérez Huber. 2016. Make America great again: Donald Trump, racist nativism and the virulent adherence to white supremecy amid US demographic change. Charleston L. Rev. 10, (2016), 215.

[35]

Kango Iwama and Yoshinobu Kano. 2018. Japanese advertising slogan generator using case frame and word vector. In Proceedings of the 11th International Conference on Natural Language Generation. 197–198.

[36]

Soo-Yeon Jeong and Young-Kuk Kim. 2021. Deep learning-based context-aware recommender system considering contextual features. Appl. Sci. 12, 1 (2021), 45.

[37]

Ziyun Jiao and Fuji Ren. 2021. WRGAN: Improvement of RelGAN with Wasserstein loss for text generation. Electronics 10, 3 (2021), 275.

[38]

Boris Katz, Igor Kaplansky, Rebecca Schulman, and Ali Ibrahim. 2000. Information retrieval using patterns and relations. MIT Artif. Intell. Lab. Res. Abst. (Sept. 2000).

[39]

Qunsheng Ke and Weiwei Wang. 2013. The adjective frequency in advertising English slogans. Theor. Pract. Lang. Stud. 3, 2 (2013).

[40]

Istvan Kecskes. 2006. On my mind: Thoughts about salience, context and figurative language from a second language perspective. Sec. Lang. Res. 22, 2 (2006), 219–237.

[41]

Md Khan, Akib Zabed, Saif Mahmud Parvez, Md Rahman, and Md Musfique Anwar. 2021. Efficient advertisement slogan detection and classification using a hierarchical BERT and BiLSTM-BERT ensemble model. In Proceedings of the International Conference on Intelligent Computing & Optimization. Springer, 964–975.

[42]

Gunhee Kim and Eric P. Xing. 2014. Reconstructing storyline graphs for image recommendation from web community photos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3882–3889.

Digital Library

[43]

Chiranjeev Kohli, Lance Leuthesser, and Rajneesh Suri. 2007. Got slogan? Guidelines for creating effective slogans. Busin. Horiz. 50, 5 (2007), 415–422.

[44]

Yingying Liu, Peipei Li, and Xuegang Hu. 2022. Combining context-relevant features with multi-stage attention network for short text classification. Comput. Speech Lang. 71, (2022), 101268.

Digital Library

[45]

Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019).

[46]

Yong Liu and Xiangnan Gou. 2021. A text classification method based on graph attention networks. In Proceedings of the International Conference on Information Technology and Biomedical Engineering (ICITBE’21). IEEE, 35–39.

[47]

Gözde Özbal, Daniele Pighin, and Carlo Strapparava. 2013. BRAINSUP: Brainstorming support for creative sentence generation. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. 1446–1455.

[48]

Steve Pan. 2019. Tourism slogans–Towards a conceptual framework. Tour. Manag. 72, (2019), 180–191.

[49]

Steve Pan, Carla Santos, and Seongseop Kim. 2017. Promoting tourism, projecting power: The role of television commercials. J. Travel Tour. Market. 34, 2 (2017), 192–208.

[50]

Yingwei Pan, Tao Mei, Ting Yao, Houqiang Li, and Yong Rui. 2016. Jointly modeling embedding and translation to bridge video and language. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4594–4602.

[51]

Xiao Pu, Lin Yuan, Jiaxu Leng, Tao Wu, and Xinbo Gao. 2023. Lexical knowledge enhanced text matching via distilled word sense disambiguation. Knowl.-based Syst. 263 (2023), 110282.

Digital Library

[52]

Israr Qureshi, Shan L. Pan, and Yingqin Zheng. 2021. Digital social innovation: An overview and research framework. Inf. Syst. J. 31, 5 (2021), 647–671.

[53]

Alexander M. Rush, Sumit Chopra, and Jason Weston. 2015. A neural attention model for abstractive sentence summarization. arXiv preprint arXiv:1509.00685 (2015).

[54]

Omar Sharif and Mohammed Moshiul Hoque. 2019. Automatic detection of suspicious Bangla text using logistic regression. In Proceedings of the International Conference on Intelligent Computing & Optimization. Springer, 581–590.

[55]

Saeid Sheikhi. 2021. An effective fake news detection method using WOA-xgbTree algorithm and content-based features. Appl. Soft Comput. 109, (2021), 107559.

Digital Library

[56]

Gunnar A. Sigurdsson, Xinlei Chen, and Abhinav Gupta. 2016. Learning visual storylines with skipping recurrent neural networks. In Proceedings of the European Conference on Computer Vision. Springer, 71–88.

[57]

Angus Stevenson. 2010. Oxford Dictionary of English. Oxford University Press, USA.

[58]

Carlo Strapparava, Alessandro Valitutti, and Oliviero Stock. 2007. Automatizing two creative functions for advertising. In Proceedings of the 4th International Joint Workshop on Computational Creativity. 99–108.

[59]

Jing Su, Qingyun Dai, Frank Guerin, and Mian Zhou. 2021. BERT-hLSTMs: BERT and hierarchical LSTMs for visual storytelling. Comput. Speech Lang. 67, (2021), 101169.

[60]

Ilya Sutskever, James Martens, and Geoffrey E. Hinton. 2011. Generating text with recurrent neural networks. In Proceedings of the International Conference on Machine Learning (ICML’11).

[61]

Yoad Tewel, Yoav Shalev, Idan Schwartz, and Lior Wolf. 2022. ZeroCap: Zero-shot image-to-text generation for visual-semantic arithmetic. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 17918–17928.

[62]

Andros Tjandra, Sakriani Sakti, Ruli Manurung, Mirna Adriani, and Satoshi Nakamura. 2016. Gated recurrent neural tensor network. In Proceedings of the International Joint Conference on Neural Networks (IJCNN’16). IEEE, 448–455.

[63]

Polona Tomašič, M. Žnidaršič, and Gregor Papa. 2014. Implementation of a slogan generator. In Proceedings of the 5th International Conference on Computational Creativity. 340–343.

[64]

Polyxeni Vassilakopoulou and Eli Hustad. 2023. Bridging digital divides: A literature review and research agenda for information systems research. Inf. Syst. Front. 25, 3 (2023), 955–969.

Digital Library

[65]

Tony Veale and Guofu Li. 2013. Creating similarity: Lateral thinking for vertical similarity judgments. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. 660–670.

[66]

Subhashini Venugopalan, Marcus Rohrbach, Jeffrey Donahue, Raymond Mooney, Trevor Darrell, and Kate Saenko. 2015. Sequence to sequence-video to text. In Proceedings of the IEEE International Conference on Computer Vision. 4534–4542.

Digital Library

[67]

Hiroaki Yamane and Masafumi Hagiwara. 2015. Advertising slogan generation system reflecting user preference on the web. In Proceedings of the IEEE 9th International Conference on Semantic Computing (IEEE ICSC’15). IEEE, 358–364.

[68]

Rui Yang, Pan Zhang, and YongGe Niu. 2018. The effect of tourism slogan message appeals on attitude toward slogan and travel intention: Based on the perspective of mental imagery processing theory. Tour. Trib. 33, 6 (2018), 73–86.

[69]

Ming Yuan and Jiangtao Ren. 2021. Numerical feature transformation-based sequence generation model for multi-disease diagnosis. Int. J. Pattern Recog. Artif. Intell. 35, 10 (2021), 2159034.

[70]

Roman Zeiss, Anne Ixmeier, Jan Recker, and Johann Kranz. 2021. Mobilising information systems scholarship for a circular economy: Review, synthesis, and directions for future research. Inf. Syst. J. 31, 1 (2021), 148–183.

[71]

Yuqi Zhang, Bin Guo, Yasan Ding, Jiaqi Liu, Chen Qiu, Sicong Liu, and Zhiwen Yu. 2022. Investigation of the determinants for misinformation correction effectiveness on social media during COVID-19 pandemic. Inf. Process. Manag. 59, 3 (2022), 102935.

Digital Library

[72]

Ahmad Zuhdi, Cecep Suryana, Rodon Pedrason, Satryo Sasono, and Ahmad Musabiq Habibie. 2023. Digital campaign: Character branding and framing towards the 2024 presidential election. Al Qalam: J. Ilmiah Keagam. Kemasyar. 17, 1 (2023), 195–208.

Cited By

Ahmad PYuanchao LAurangzeb KAnwar MHaq Q(2024)Semantic web-based propaganda text detection from social media using meta-learningService Oriented Computing and Applications10.1007/s11761-024-00422-xOnline publication date: 2-Aug-2024
https://doi.org/10.1007/s11761-024-00422-x

Index Terms

Enhancing Coherence and Diversity in Multi-class Slogan Generation Systems
1. Information systems
  1. Information systems applications
    1. Data mining

Recommendations

Addressing class-imbalance in multi-label learning via two-stage multi-label hypernetwork

Multi-label learning is concerned with learning from data examples that are represented by a single feature vector while associated with multiple labels simultaneously. Existing multi-label learning approaches mainly focus on exploiting label ...
Dynamic Label Propagation for Semi-supervised Multi-class Multi-label Classification
ICCV '13: Proceedings of the 2013 IEEE International Conference on Computer Vision

In graph-based semi-supervised learning approaches, the classification rate is highly dependent on the size of the availabel labeled data, as well as the accuracy of the similarity measures. Here, we propose a semi-supervised multi-class/multi-label ...
Semi-supervised boosting for multi-class classification
ECMLPKDD'08: Proceedings of the 2008th European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II

Most semi-supervised learning algorithms have been designed for binary classification, and are extended to multi-class classification by approaches such as one-against-the-rest. The main shortcoming of these approaches is that they are unable to exploit ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 23, Issue 8

August 2024

343 pages

EISSN:2375-4702

DOI:10.1145/3613611

Editor:
Imed Zitouni
Google, USA
,
Guest Editors:
Deepak Kumar Jain,
Thierry Boumans,
Stefano Berretti

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 August 2024

Online AM: 15 December 2023

Accepted: 12 December 2023

Revised: 10 November 2023

Received: 20 September 2023

Published in TALLIP Volume 23, Issue 8

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Natural Science Foundation of China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
266
Total Downloads

Downloads (Last 12 months)266
Downloads (Last 6 weeks)19

Reflects downloads up to 18 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Ahmad PYuanchao LAurangzeb KAnwar MHaq Q(2024)Semantic web-based propaganda text detection from social media using meta-learningService Oriented Computing and Applications10.1007/s11761-024-00422-xOnline publication date: 2-Aug-2024
https://doi.org/10.1007/s11761-024-00422-x

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents