Topic-driven Ensemble for Online Advertising Generation

Egor Nevezhin, Nikolay Butakov, Maria Khodorchenko, Maxim Petrov, Denis Nasonov


Abstract
Online advertising is one of the most widespread ways to reach and increase a target audience for those selling products. Usually having a form of a banner, advertising engages users into visiting a corresponding webpage. Professional generation of banners requires creative and writing skills and a basic understanding of target products. The great variety of goods presented in the online market enforce professionals to spend more and more time creating new advertisements different from existing ones. In this paper, we propose a neural network-based approach for the automatic generation of online advertising using texts from given webpages as sources. The important part of the approach is training on open data available online, which allows avoiding costly procedures of manual labeling. Collected open data consist of multiple subdomains with high data heterogeneity. The subdomains belong to different topics and vary in used vocabularies, phrases, styles that lead to reduced quality in adverts generation. We try to solve the problem of identifying existed subdomains and proposing a new ensemble approach based on exploiting multiple instances of a seq2seq model. Our experimental study on a dataset in the Russian language shows that our approach can significantly improve the quality of adverts generation.
Anthology ID:
2020.coling-main.206
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2273–2283
Language:
URL:
https://aclanthology.org/2020.coling-main.206
DOI:
10.18653/v1/2020.coling-main.206
Bibkey:
Cite (ACL):
Egor Nevezhin, Nikolay Butakov, Maria Khodorchenko, Maxim Petrov, and Denis Nasonov. 2020. Topic-driven Ensemble for Online Advertising Generation. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2273–2283, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Topic-driven Ensemble for Online Advertising Generation (Nevezhin et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.206.pdf
Data
MS MARCO