No ethics section, but there are ethical issues that deserve discussion (see the ethics section).
Also a few, mostly minor points:
- When the corpus was created, participants were told to speak in such a way to make the intent of the speech unambiguous. This may lead to over-emphasis compared with natural speech. There was no mention of any evaluation of the data to avoid this.
- The corpus was created with only ambiguous sentences, and the non-ambiguous content was taken from another source. There is a chance that different recording qualities between news (LSCVR) and crowdsourced data could artificially raise the ability of the model to distinguish between ambiguous (tag 1 or 2) and non-ambiguous (tag 0) sentences.
- The amount of data used to train the text disambiguation model was significantly lower than the data used for training the end-to-end system. Given that the difference between the two proposed systems is only a few percentage points, it brings into question the conclusion that the direct model is clearly the better of the two (but they still are both demonstrably superior to the baseline).
- It would be hard to reproduce the fine tuning of the IndoBART model without a little more information. Was it fine-tuned for a certain number of steps, for example?
- The amount of data used to train the text disambiguation model was significantly lower than the data used for training the end-to-end system. Given that the difference between the two proposed systems is only a few percentage points, it brings into question the conclusion that the direct model is clearly the better of the two (but they still are both demonstrably superior to the baseline).