Evaluating and Explaining Natural Language Generation with GenX

Kayla Duskin, Shivam Sharma, Ji Young Yun, Emily Saldanha, Dustin Arendt


Abstract
Current methods for evaluation of natural language generation models focus on measuring text quality but fail to probe the model creativity, i.e., its ability to generate novel but coherent text sequences not seen in the training corpus. We present the GenX tool which is designed to enable interactive exploration and explanation of natural language generation outputs with a focus on the detection of memorization. We demonstrate the utility of the tool on two domain-conditioned generation use cases - phishing emails and ACL abstracts.
Anthology ID:
2021.dash-1.12
Volume:
Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances
Month:
June
Year:
2021
Address:
Online
Venues:
DaSH | NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
70–78
Language:
URL:
https://aclanthology.org/2021.dash-1.12
DOI:
10.18653/v1/2021.dash-1.12
Bibkey:
Cite (ACL):
Kayla Duskin, Shivam Sharma, Ji Young Yun, Emily Saldanha, and Dustin Arendt. 2021. Evaluating and Explaining Natural Language Generation with GenX. In Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances, pages 70–78, Online. Association for Computational Linguistics.
Cite (Informal):
Evaluating and Explaining Natural Language Generation with GenX (Duskin et al., DaSH 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.dash-1.12.pdf
Code
 pnnl/genx