- It lacks novelty that I would expect from a long research paper. It is read more like a report, rather than a research paper. - The paper could be restructured and some parts could be better written. Please see comments and suggestions for more details. - Some contributions are not clear, e.g., which datasets were available, which are released (what kind of postprocessing is done)? hard to digest this from text. Some techniques that could be a contribution remain somehow hidden in lines 425-462, and some are not clear. - Most of the evaluation tasks are already well-known, but had been executed separately. Morpheme-based tasks are more relevant to multilingual probing literature (e.g., LINSPECTOR: Multilingual Probing Tasks for Word Representations) which are not mentioned throughout the paper.
- 091: I don't understand how this is relevant to the paper. It is mentioned a couple of times however I haven't seen such an effort, maybe I'm missing something? - 096: typo: MLR -> MRL - 121: Not clear if you propose these tasks or only combine them into a pipeline? You could write your contributions as bullet points. - 156: "objective objectives" sounds weird.
- Section 2 can be condensed quite a lot. 133-156 is already known, so too long. I'd be more interested to see a review on multilingual benchmarks and probing tasks, especially for morphologically rich languages. I'd rather see more details of the other Hebrew models. - 174-176: Again sounds like the authors take GLUE (or similar benchmark) and translate the tasks into Hebrew. - 178:181: But there is a rich literature on morphological and syntactic probing tasks (e.g., LINSPECTOR: Multilingual Probing Tasks for Word Representations) - Throughout the paper, the authors use the term NLU, however I'm not sure whether some of the lower level tasks count as NLU or syntactic tasks. - 254: Oscar is introduced here but there is no description until line 276.
- 313 - footnote 2: What's the postprocessing effort here, not clear. - 320-346: This could be a table.
- 330: Introduce SPMR, what does it stand for?
- 332: which UD version?
- Table 3: The english transcriptions are needed - footnote 4,5: what's the added value (if any)? why is 5 anonymous, didn't understand?
- 428-448: I didn't understand how you feed the subword or token representation to a char-LSTM? Doesn't your model need the "char" representation?
- 636-638: Language-agnostic is over claiming here. - Why Aleph?
- 313 - footnote 2: What's the postprocessing effort here, not clear.