@inproceedings{fu-etal-2020-dynamic,
title = "Dynamic Topic Tracker for {KB}-to-Text Generation",
author = "Fu, Zihao and
Bing, Lidong and
Lam, Wai and
Jameel, Shoaib",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.215",
doi = "10.18653/v1/2020.coling-main.215",
pages = "2369--2380",
abstract = "Recently, many KB-to-text generation tasks have been proposed to bridge the gap between knowledge bases and natural language by directly converting a group of knowledge base triples into human-readable sentences. However, most of the existing models suffer from the off-topic problem, namely, the models are prone to generate some unrelated clauses that are somehow involved with certain input terms regardless of the given input data. This problem seriously degrades the quality of the generation results. In this paper, we propose a novel dynamic topic tracker for solving this problem. Different from existing models, our proposed model learns a global hidden representation for topics and recognizes the corresponding topic during each generation step. The recognized topic is used as additional information to guide the generation process and thus alleviates the off-topic problem. The experimental results show that our proposed model can enhance the performance of sentence generation and the off-topic problem is significantly mitigated.",
}
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<abstract>Recently, many KB-to-text generation tasks have been proposed to bridge the gap between knowledge bases and natural language by directly converting a group of knowledge base triples into human-readable sentences. However, most of the existing models suffer from the off-topic problem, namely, the models are prone to generate some unrelated clauses that are somehow involved with certain input terms regardless of the given input data. This problem seriously degrades the quality of the generation results. In this paper, we propose a novel dynamic topic tracker for solving this problem. Different from existing models, our proposed model learns a global hidden representation for topics and recognizes the corresponding topic during each generation step. The recognized topic is used as additional information to guide the generation process and thus alleviates the off-topic problem. The experimental results show that our proposed model can enhance the performance of sentence generation and the off-topic problem is significantly mitigated.</abstract>
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%0 Conference Proceedings
%T Dynamic Topic Tracker for KB-to-Text Generation
%A Fu, Zihao
%A Bing, Lidong
%A Lam, Wai
%A Jameel, Shoaib
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F fu-etal-2020-dynamic
%X Recently, many KB-to-text generation tasks have been proposed to bridge the gap between knowledge bases and natural language by directly converting a group of knowledge base triples into human-readable sentences. However, most of the existing models suffer from the off-topic problem, namely, the models are prone to generate some unrelated clauses that are somehow involved with certain input terms regardless of the given input data. This problem seriously degrades the quality of the generation results. In this paper, we propose a novel dynamic topic tracker for solving this problem. Different from existing models, our proposed model learns a global hidden representation for topics and recognizes the corresponding topic during each generation step. The recognized topic is used as additional information to guide the generation process and thus alleviates the off-topic problem. The experimental results show that our proposed model can enhance the performance of sentence generation and the off-topic problem is significantly mitigated.
%R 10.18653/v1/2020.coling-main.215
%U https://aclanthology.org/2020.coling-main.215
%U https://doi.org/10.18653/v1/2020.coling-main.215
%P 2369-2380
Markdown (Informal)
[Dynamic Topic Tracker for KB-to-Text Generation](https://aclanthology.org/2020.coling-main.215) (Fu et al., COLING 2020)
ACL
- Zihao Fu, Lidong Bing, Wai Lam, and Shoaib Jameel. 2020. Dynamic Topic Tracker for KB-to-Text Generation. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2369–2380, Barcelona, Spain (Online). International Committee on Computational Linguistics.