Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to...

This paper proposes the Phrase-BERT which can produce more powerful phrase embeddings by averaging token embeddings and learn them through contrastive learning. Positive samples are created by a paraphrasing model and negative samples are created by random sampling from a pre-defined set of frequent phrases.


  • Not too much surprising but it’s a good work.
  • Why do you replace the occurrence of phrases with a MASK token? No explanation regarding this.
  • Your loss function does not push p^{+} against p^{-} directly but push them against p which might lead to collision of them.
  • 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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