Comments (1)
Thanks for your outstanding work!
In the End2End evaluation, the PPTOD-large is worse than PPTOD-small and PPTOD-base on both full-training (Table 2) and few-shot (Table 3) versions.
To a certain extent, this result is contrary to common sense. Is there any possible reason or experimental discovery?
Hi,
Many thanks for your interest in our work!
For the PPTOD-large's performance in E2E modelling, we provide some analysis in our ACL camera-ready version which is available here (https://arxiv.org/pdf/2109.14739.pdf). Please find our analysis in section 4.1.3.
Please let me know if you have further questions :-)
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Related Issues (18)
- About results of the E2E-TOD HOT 2
- About DST on delexicalized response HOT 3
- How to pretrain from scratch? HOT 1
- About domain overlapping HOT 1
- About evaluation script HOT 1
- E2E_TOD performance
- Question about low-resource settings due to potential leaking information from Pre-training Phase
- Can i use sentencepiece tokenizer model as pertained ?
- Issues with E2E modelling HOT 7
- About E2E_Tod Modeling
- Not able to download data HOT 1
- DST inference result on MultiWOZ 2.1 HOT 2
- Plug-and-Play vs Cascaded Generation HOT 5
- How to lexicalize the generated responses? HOT 1
- Quesation about reproducing results using my own model HOT 5
- Performance impact of `shuffle_mode`? HOT 2
- more model needed HOT 1
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