Extending Multilingual Machine Translation through Behavioral Cloning
Authors: Wen Lai, Viktor Hangya, Yingli Shen, Alexander Fraser


Reviewer paper summaries: This paper proposes a novel method to extend the existing multilingual neural machine translation model to a new language without sacrificing performance on existing language pairs. The main procedure can be summarized as employing an expert model to generate pseudo translations from "the corresponding English translation of the new language" to "several existing languages" and use these generated data to fine-tune a new model, which is initialized just from the expert model.
Summary of strengths: The idea is solid and the re-weighting mechanism carefully handles the good and bad pseudo data. The writing is also easy-to-understand.
Summary of weaknesses: 1. Just want to know if I understand your idea correctly. According to your description in L255 and L372, does the on-the-fly method differ from your method of parameter updating? I mean, your method employs two models, the expert model and the new model, and you only update the new model. But for the on-the-fly method, the expert model and the new model are the same, so the generated pseudo translations will change along with the training. Did I understand your method correctly?
    1. if yes, then I think you should add more description on this part, it is a little bit confusing.
1. I am surprised by the big performance gap between the on-the-fly method and your method. You only displayed the numbers in these Tables and your hypothesis on L427, and own this to "degrades the quality of the generated pseudo-corpus". But this claim is not convincing, could you please show me some examples here? For example, select a sentence from the new language, what pseudo translation we can get from its English translation at the different training stages? Before training, at the 1000th step, the 5000th step, the 10000th step, and so on until the end of training? I mean how does pseudo-corpus degrades?
    1. If this concern is properly answered and the authors promise these updates will be reflected in the final version, I am open to raise my score.
Comments, suggestions and typos: 1. Some LaTeX formulas should be polished, for example, L317, why do you use a different symbol instead of $\ell$ to refer to the sampled language?
2. For subscripts and superscribes, it is better to distinguish variable and label, for variables it's okay to use the plain text, e.g., use `\ell_{i}` to get $\ell_{i}$, but for labels, using `\text{eng}` is a much better choice, i.e., use `\ell_{\text{eng}}` to get $\ell_{\text{eng}}$.
Overall assessment scores: 3.5
Metareview score: 3
Metareview: Adding a new language to a pre-trained multilingual machine translation model can be a daunting task. Existing methods suffer from catastrophic forgetting where performance on already seen languages deteriorates with further finetuning on new languages. This work proposes BC-MNMT where the task of learning a new language is treated as a “behavioral cloning process”. BC-MNMT relies on constructing a pseudo multi-parallel corpus of the new and original languages pivoting through English. The model will then be encouraged to imitate the output distribution of the original MNMT model on already seen languages.
Language-weighted entropy loss is used to optimize model parameters. The weights are derived from the BLEU score of each language pair in the original model as a proxy for the quality of the pseudo-labeled data.
Results on 8 low-resourced languages to extend the coverage of M2M-100 show good performance with mitigated catastrophic forgetting.
Summary of reasons to publish: - This work proposes an interesting weighing scheme to balance pseudo-labeled data in different languages with highly variable translation quality. 
- Results show reasonable improvements with BC-MNMT over standard KD without language-weighted loss or the on-the-fly approach (the student itself generates the progressively pseudo-labeled data) , all finetuned on similarly pseudo-labeled corpora.