Generating Slogans with Linguistic Features using Sequence-to-Sequence Transformer

Yeoun Yi, Hyopil Shin


Abstract
Previous work generating slogans depended on templates or summaries of company descriptions, making it difficult to generate slogans with linguistic features. We present LexPOS, a sequence-to-sequence transformer model that generates slogans given phonetic and structural information. Our model searches for phonetically similar words given user keywords. Both the sound-alike words and user keywords become lexical constraints for generation. For structural repetition, we use POS constraints. Users can specify any repeated phrase structure by POS tags. Our model-generated slogans are more relevant to the original slogans than those of baseline models. They also show phonetic and structural repetition during inference, representative features of memorable slogans.
Anthology ID:
2021.icon-main.10
Volume:
Proceedings of the 18th International Conference on Natural Language Processing (ICON)
Month:
December
Year:
2021
Address:
National Institute of Technology Silchar, Silchar, India
Editors:
Sivaji Bandyopadhyay, Sobha Lalitha Devi, Pushpak Bhattacharyya
Venue:
ICON
SIG:
Publisher:
NLP Association of India (NLPAI)
Note:
Pages:
75–79
Language:
URL:
https://aclanthology.org/2021.icon-main.10
DOI:
Bibkey:
Cite (ACL):
Yeoun Yi and Hyopil Shin. 2021. Generating Slogans with Linguistic Features using Sequence-to-Sequence Transformer. In Proceedings of the 18th International Conference on Natural Language Processing (ICON), pages 75–79, National Institute of Technology Silchar, Silchar, India. NLP Association of India (NLPAI).
Cite (Informal):
Generating Slogans with Linguistic Features using Sequence-to-Sequence Transformer (Yi & Shin, ICON 2021)
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PDF:
https://aclanthology.org/2021.icon-main.10.pdf