LIMIT-BERT : Linguistics Informed Multi-Task BERT

This paper inject linguistic knowledge into BERT pre-training with semi-supervised MTL on core NLP tasks like POS/NER/DEP/SRL/CON.


  • To parse large-scale corpus is very expensive, so is adding lots of decoders into the PLM.
  • The boost by semi-supervised pre-training seems to be quite small (DEP +0.22, CON+0.08, SRL+0.66). And the highest scores on these tasks fall behind SOTA for large margins. So how to convince us that these semi-supervised learning tasks worth their costs?
  • 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.

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