This paper proposes to use pointer networks to predict the spans of aspect and opinion for Aspect Sentiment Triplet Extraction. Their pointer network is made of a BiLSTM with 2 MLPs to predict start and end offsets.
Comments
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The approach section is not intuitive. Lots of details are missing and the authors should not really put them into appendix.
- How do you predict the numbers of Aspect-Opinion-Sentiment triplets?
- How do you embed the dependency tree features per each token?
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Given the massive number of questions left even after several rounds of reading, I’d suggest this paper to be revised for another round.
Rating
- 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 投票人