before_sent:  To alleviate this problem, we propose a new metric for evaluating the correctness of genQA . Specifically, the new metric assigns different weights on each token via keyphrase prediction, thereby judging whether a predicted answer sentence captures the key meaning of the human judge's ground-truth .
before_sent_with_intent: <clarity>  To alleviate this problem, we propose a new metric for evaluating the correctness of genQA . Specifically, the new metric assigns different weights on each token via keyphrase prediction, thereby judging whether a predicted answer sentence captures the key meaning of the human judge's ground-truth .
after_sent:  To alleviate this problem, we propose a new metric for evaluating the correctness of genQA . Specifically, our new metric assigns different weights on each token via keyphrase prediction, thereby judging whether a predicted answer sentence captures the key meaning of the human judge's ground-truth .