This dataset concludes source data which is professional fact descriptions from Chinese law office website and target data which is non-professional fact descriptions from daily spoken language.

The dataset is for transfer learning in law domain.
The dataset also concludes processed dictionary and .npy files.
The task of transfer learning is to predict the accusation based on the description.

Dataset Files

You must be an IEEE Dataport Subscriber to access these files. Subscribe now or login.

[1] Guangyi Xiao, Xinlong Liu, Xu Han, "Domain Adaptation in law:professional fact descriptions to non-professional fact descriptions", IEEE Dataport, 2020. [Online]. Available: http://dx.doi.org/10.21227/3gaa-2s33. Accessed: Apr. 22, 2024.
@data{3gaa-2s33-20,
doi = {10.21227/3gaa-2s33},
url = {http://dx.doi.org/10.21227/3gaa-2s33},
author = {Guangyi Xiao; Xinlong Liu; Xu Han },
publisher = {IEEE Dataport},
title = {Domain Adaptation in law:professional fact descriptions to non-professional fact descriptions},
year = {2020} }
TY - DATA
T1 - Domain Adaptation in law:professional fact descriptions to non-professional fact descriptions
AU - Guangyi Xiao; Xinlong Liu; Xu Han
PY - 2020
PB - IEEE Dataport
UR - 10.21227/3gaa-2s33
ER -
Guangyi Xiao, Xinlong Liu, Xu Han. (2020). Domain Adaptation in law:professional fact descriptions to non-professional fact descriptions. IEEE Dataport. http://dx.doi.org/10.21227/3gaa-2s33
Guangyi Xiao, Xinlong Liu, Xu Han, 2020. Domain Adaptation in law:professional fact descriptions to non-professional fact descriptions. Available at: http://dx.doi.org/10.21227/3gaa-2s33.
Guangyi Xiao, Xinlong Liu, Xu Han. (2020). "Domain Adaptation in law:professional fact descriptions to non-professional fact descriptions." Web.
1. Guangyi Xiao, Xinlong Liu, Xu Han. Domain Adaptation in law:professional fact descriptions to non-professional fact descriptions [Internet]. IEEE Dataport; 2020. Available from : http://dx.doi.org/10.21227/3gaa-2s33
Guangyi Xiao, Xinlong Liu, Xu Han. "Domain Adaptation in law:professional fact descriptions to non-professional fact descriptions." doi: 10.21227/3gaa-2s33