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Source code for the EMNLP 2020 paper "Cold-Start and Interpretability: Turning Regular Expressions intoTrainable Recurrent Neural Networks"
Hi, I want to reproduce the results in the paper!So, can you publish the optimal parameter settings on different datasets?
Hello, thank you very much for your beautiful work. I want to study your paper. I've created the directory as the Readme, but, when I execute "python data.py --dataset ATIS" it throw ERROR:
I checked the data.py, but I still don't understand what “atis.{}.new.pkl” means.
Waiting for your help, thanks.
1.当状态数非常大时,比如K=750, 张量秩分解的误差非常大,应该如何解决?
2.当K=750时,我们的rank应该选取多少比较合适?
Hello, thank you very much for your work, it is very meaningful, but I have some questions to ask you. Can I use my own data set to replace it? And if so, how should I do it? Looking forward to your reply.
你好。论文里提到了将多个WFAs合并成一个,具体是怎么操作的呢,比如对于V_embedding词嵌入向量,shape为(V,r),那是把r分配到每个子WFA吗。此外,对于D1,D2,我的猜想是将根据子WFA的状态states数对应的赋值到r*K的矩阵里面,我的猜想对吗?
请问你有对比过Marrying Up Regular Expressions with Neural Networks这篇中attention guide的结果吗?
也就是那篇论文中Network Module Level上+two+both的结果,谢谢~
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