  A crucial issue of current text generation models is that they often
uncontrollably generate factually inconsistent text with respective of their
inputs. Limited by the lack of annotated data, existing works in evaluating
factual consistency directly transfer the reasoning ability of models trained
on other data-rich upstream tasks like question answering (QA) and natural
language inference (NLI) without any further adaptation. As a result, they
perform poorly on the real generated text and are biased heavily by their
single-source upstream tasks. To alleviate this problem, we propose a weakly
supervised framework that aggregates multiple resources to train a precise and
efficient factual metric, namely WeCheck. WeCheck first utilizes a generative
model to accurately label a real generated sample by aggregating its weak
labels, which are inferred from multiple resources. Then, we train the target
metric model with the weak supervision while taking noises into consideration.
Comprehensive experiments on a variety of tasks demonstrate the strong
performance of WeCheck, which achieves a 3.4\% absolute improvement over
previous state-of-the-art methods on TRUE benchmark on average.
