@inproceedings{xie-etal-2019-blcu,
title = "{BLCU}{\_}{NLP} at {S}em{E}val-2019 Task 8: A Contextual Knowledge-enhanced {GPT} Model for Fact Checking",
author = "Xie, Wanying and
Que, Mengxi and
Yang, Ruoyao and
Liu, Chunhua and
Yu, Dong",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2198",
doi = "10.18653/v1/S19-2198",
pages = "1132--1137",
abstract = "Since the resources of Community Question Answering are abundant and information sharing becomes universal, it will be increasingly difficult to find factual information for questioners in massive messages. SemEval 2019 task 8 is focusing on these issues. We participate in the task and use Generative Pre-trained Transformer (OpenAI GPT) as our system. Our innovations are data extension, feature extraction, and input transformation. For contextual knowledge enhancement, we extend the training set of subtask A, use several features to improve the results of our system and adapt the input formats to be more suitable for this task. We demonstrate the effectiveness of our approaches, which achieves 81.95{\%} of subtask A and 61.08{\%} of subtask B in accuracy on the SemEval 2019 task 8.",
}
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<abstract>Since the resources of Community Question Answering are abundant and information sharing becomes universal, it will be increasingly difficult to find factual information for questioners in massive messages. SemEval 2019 task 8 is focusing on these issues. We participate in the task and use Generative Pre-trained Transformer (OpenAI GPT) as our system. Our innovations are data extension, feature extraction, and input transformation. For contextual knowledge enhancement, we extend the training set of subtask A, use several features to improve the results of our system and adapt the input formats to be more suitable for this task. We demonstrate the effectiveness of our approaches, which achieves 81.95% of subtask A and 61.08% of subtask B in accuracy on the SemEval 2019 task 8.</abstract>
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%0 Conference Proceedings
%T BLCU_NLP at SemEval-2019 Task 8: A Contextual Knowledge-enhanced GPT Model for Fact Checking
%A Xie, Wanying
%A Que, Mengxi
%A Yang, Ruoyao
%A Liu, Chunhua
%A Yu, Dong
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F xie-etal-2019-blcu
%X Since the resources of Community Question Answering are abundant and information sharing becomes universal, it will be increasingly difficult to find factual information for questioners in massive messages. SemEval 2019 task 8 is focusing on these issues. We participate in the task and use Generative Pre-trained Transformer (OpenAI GPT) as our system. Our innovations are data extension, feature extraction, and input transformation. For contextual knowledge enhancement, we extend the training set of subtask A, use several features to improve the results of our system and adapt the input formats to be more suitable for this task. We demonstrate the effectiveness of our approaches, which achieves 81.95% of subtask A and 61.08% of subtask B in accuracy on the SemEval 2019 task 8.
%R 10.18653/v1/S19-2198
%U https://aclanthology.org/S19-2198
%U https://doi.org/10.18653/v1/S19-2198
%P 1132-1137
Markdown (Informal)
[BLCU_NLP at SemEval-2019 Task 8: A Contextual Knowledge-enhanced GPT Model for Fact Checking](https://aclanthology.org/S19-2198) (Xie et al., SemEval 2019)
ACL