CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language...

This paper proposed multi-task pre-training with NLU and NLG sharing the same transformer encoder and appeared to obtain good performance on both sides.



  • Although techniques are obsolete, idea is good.
  • It saves time to pre-train both NLU and NLG and seems that no interferences occurred.
  • 5: Transformative: This paper is likely to change our field. It should be considered for a best paper award.
  • 4.5: Exciting: It changed my thinking on this topic. I would fight for it to be accepted.
  • 4: Strong: I learned a lot from it. I would like to see it accepted.
  • 3.5: Leaning positive: It can be accepted more or less in its current form. However, the work it describes is not particularly exciting and/or inspiring, so it will not be a big loss if people don’t see it in this conference.
  • 3: Ambivalent: It has merits (e.g., it reports state-of-the-art results, the idea is nice), but there are key weaknesses (e.g., I didn’t learn much from it, evaluation is not convincing, it describes incremental work). I believe it can significantly benefit from another round of revision, but I won’t object to accepting it if my co-reviewers are willing to champion it.
  • 2.5: Leaning negative: I am leaning towards rejection, but I can be persuaded if my co-reviewers think otherwise.
  • 2: Mediocre: I would rather not see it in the conference.
  • 1.5: Weak: I am pretty confident that it should be rejected.
  • 1: Poor: I would fight to have it rejected.

0 投票者

Did a benchmark on CSL generation dataset and CPT-base performed poorly. Its rouge-l is 56.54 while Mengzi’s is 67.07. I guess their idea is good, but BART is somewhat inferior to T5.

Besides, you can init BART encoder for NLG just like what Mengzi did. So MTL is not a requirement.