Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: Good paper however reproducible code missing 
strengths_and_weaknesses: 1. The paper is well written and addresses low resource languages 
2. pivot pre-finetuning ideally using a pretrained language model
3. Performance metrics with 3 different levels of resource languages(low,mid,high) along side with human translation which is best way to go
4. Only drawback is reproducible code not provided in the paper
suggested_changes: Few corrections are as advised on this paper
1. Spellcheck few words are misspelled like the word “ language” misspelled in the first paragraph 
2. Always Ensure complete words are in a single line, for instance due to shortage of space word “intermediate” is split into intermedi- in one line & ate in next line
3. link to the code of the research paper is not provided, unable to determine validity of this paper
review_title: null

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: The paper proposes to use Neubig & Hu (2018)’s rapid NMT approach for English-to-Kikamba Machine Translation, an extremely low-resource language pair.
strengths_and_weaknesses: Strengths: The paper investigates an important and challenging problem in very low-resource MT.

Weaknesses: The paper lacks clarity on the description of the experimental setup and seems hard to reproduce with very less details about the training data.
suggested_changes: 1. It is unclear how the sizes of training data in Table 1 map to the training dataset discussed in A.1 Adding explicit description on what training data is used and how will be helpful.

2. Table 2 conflates the accuracy of translations with their fluency. Maybe using the sQMT framework [1, Section 3.2] for human annotation is what you need.

[1] [Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation](https://arxiv.org/abs/2104.14478)
review_title: null

Meta-review: metareview: **Summary**
* The paper proposes to use Neubig & Hu (2018)’s rapid NMT approach for English-to-Kikamba Machine Translation, an extremely low-resource language pair.

**Strengths**
* The paper investigates an important MT problem in low-resource settings.
* Used a rigorous performance metric that includes 3 different levels of resource languages (low, mid, and high) alongside the human translation.

**Weakness**
* The results are hard to reproduce with very less details about the training data, and the code is not shared.

confidence: 4: The area chair is confident but not absolutely certain
Decision: Invite to archive