This paper presents the winner system Alquist 4.0 in the Alexa Prize Socialbot Grand Challenge 4. It brings two major innovations. The first one is that they propose a hybrid approach combing hand-designed responses and a NLG model. They classifier each utterance as in-domain which follows hand-designed dialog flow while out-of-domain triggers NLG. The second innovation is that they use regular expressions (skimmer) to extract user profile and incorporate these with KBs in the later dialog.
Comments
- The overall design of dialog bot is quite competitive in that it has almost every essence that a modern bot would have: user profile, KB, NLG, intent classifier. And it’s amazing to see they put them together and get them to work.
- Their carefully selected corpus (reddit trivia, question vs statement) also reflects their expertise in QA.
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 voters