@inproceedings{lecorve-etal-2022-sparql,
title = "{SPARQL}-to-Text Question Generation for Knowledge-Based Conversational Applications",
author = "Lecorv{\'e}, Gw{\'e}nol{\'e} and
Veyret, Morgan and
Brabant, Quentin and
Rojas Barahona, Lina M.",
editor = "He, Yulan and
Ji, Heng and
Li, Sujian and
Liu, Yang and
Chang, Chua-Hui",
booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = nov,
year = "2022",
address = "Online only",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.aacl-main.11",
pages = "131--147",
abstract = "This paper focuses on the generation of natural language questions based on SPARQL queries, with an emphasis on conversational use cases (follow-up question-answering). It studies what can be achieved so far based on current deep learning models (namely pretrained T5 and BART models). To do so, 4 knowledge-based QA corpora have been homogenized for the task and a new challenge set is introduced. A first series of experiments analyzes the impact of different training setups, while a second series seeks to understand what is still difficult for these models. The results from automatic metrics and human evaluation show that simple questions and frequent templates of SPARQL queries are usually well processed whereas complex questions and conversational dimensions (coreferences and ellipses) are still difficult to handle. The experimental material is publicly available on \url{https://github.com/Orange-OpenSource/sparql-to-text} .",
}
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%0 Conference Proceedings
%T SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications
%A Lecorvé, Gwénolé
%A Veyret, Morgan
%A Brabant, Quentin
%A Rojas Barahona, Lina M.
%Y He, Yulan
%Y Ji, Heng
%Y Li, Sujian
%Y Liu, Yang
%Y Chang, Chua-Hui
%S Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2022
%8 November
%I Association for Computational Linguistics
%C Online only
%F lecorve-etal-2022-sparql
%X This paper focuses on the generation of natural language questions based on SPARQL queries, with an emphasis on conversational use cases (follow-up question-answering). It studies what can be achieved so far based on current deep learning models (namely pretrained T5 and BART models). To do so, 4 knowledge-based QA corpora have been homogenized for the task and a new challenge set is introduced. A first series of experiments analyzes the impact of different training setups, while a second series seeks to understand what is still difficult for these models. The results from automatic metrics and human evaluation show that simple questions and frequent templates of SPARQL queries are usually well processed whereas complex questions and conversational dimensions (coreferences and ellipses) are still difficult to handle. The experimental material is publicly available on https://github.com/Orange-OpenSource/sparql-to-text .
%U https://aclanthology.org/2022.aacl-main.11
%P 131-147
Markdown (Informal)
[SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications](https://aclanthology.org/2022.aacl-main.11) (Lecorvé et al., AACL-IJCNLP 2022)
ACL
- Gwénolé Lecorvé, Morgan Veyret, Quentin Brabant, and Lina M. Rojas Barahona. 2022. SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 131–147, Online only. Association for Computational Linguistics.