Weights for 'right' and 'wrong' are both negative?

[Deleted user]

Hi. First of all, thanks for making those codes public. While I was running the codes and data on my laptop, I found the experiment results are a little bit strange and counterintuitive. Though the recall error rate is quite similar to the results in paper, some other feature values really confused me:

1. I used the preset features for hlr: 'right' and 'wrong'. But when I read the model weights for these two features, it showed that both are negative (I have run the codes many times to confirm this). That means under the same circumstances, the more right answers you got from past history, the smaller half life you got, which means the more easily you would forget this word??

else: # fv.append((intern('right'), right)) # fv.append((intern('wrong'), wrong)) fv.append((intern('right'), math.sqrt(1+right))) fv.append((intern('wrong'), math.sqrt(1+wrong)))

2. When I used the model created in the experiment to predict all the half life of 13 million data, it shows that over 90% of them have a half life longer than 120 days? Does that mean averagely people need more than 4 months to likely forget a foreign word? This seems a little counterintuitive for me.