1. Although the structural information has not been explicitly used in the current problem statement, it has been implicitly used in few previous works on bilingual mapping induction. Please see:
"Multi-Stage Framework with Refinement based Point Set Registration for Unsupervised Bi-Lingual Word Alignment". Oprea et al., COLING 2022.
"Point Set Registration for Unsupervised Bilingual Lexicon Induction". H. Cao and T. Zhao, IJCAI 2018.
As such, a proper discussion and comparison is necessary in the current paper.
2. The GCN that is proposed has no learnable parameters, so it is a static matrix transformation operation. So, why is the term GCN used - isn't it misleading? Further, if it is not learning anything, it is a simple aggregation function, as far as I understood. Then the structural information is not propagating through the entire graph - this is counter-intuitive because the author(s) claim that structural information usage is a key feature of the proposed framework. Am I missing something here?
3. For experiments, I have 2 comments - (i) addition of performance on word similarity and sentence translation tasks as in the MUSE paper (and others) would lend more credibility to the robustness and effectiveness of the framework. (ii) addition of morphologically rich languages like Finnish, Hebrew, etc and low-resource languages in the experiments would be good to have (minor point).
3. For experiments, I have 2 comments - (i) addition of performance on word similarity and sentence translation tasks as in the MUSE paper (and others) would lend more credibility to the robustness and effectiveness of the framework. (ii) addition of morphologically rich languages like Finnish, Hebrew, etc and low-resource languages in the experiments would be good to have (minor point).