Empirical observations on the instability of aligning word vector spaces with GANs
Unsupervised bilingual dictionary induction (UBDI) is useful for unsupervised machine translation and for cross-lingual transfer of models into low-resource languages. One approach to UBDI is to align word vector spaces in different languages using Generative adversarial networks (GANs) with linear generators, achieving state-of-the-art performance for several language pairs. For some pairs, however, GAN-based induction is unstable or completely fails to align the vector spaces. We focus on cases where linear transformations provably exist, but the performance of GAN-based UBDI depends heavily on the model initialization. We show that the instability depends on the shape and density of the vector sets, but not on noise; it is the result of local optima, but neither over-parameterization nor changing the batch size or the learning rate consistently reduces instability. Nevertheless, we can stabilize GAN-based UBDI through best-of-N model selection, based on an unsupervised stopping criterion.
 This paper is clear and well written, except for the occasional typos, eg. missing a bracket in the very first equation. The problem of learning transformations to align vector spaces across languages is extremely interesting and relevant. While the paper does a thorough job in explaining the metrics used and in detailing the procedure followed, I would have liked to see the following the details in the paper, 1)Number of unique word tokens in Bengali, Cebuano etc. While clicking on the reference link to MUSE provides these details, including the same in the paper, would make the writing wholesome. 2)While a thorough theoretical analysis is provided to explain the quality of the transformation/rotation matrix, there are no empirical results provided on any cross-lingual classification tasks. While not too important to the paper, such an analysis would provide the reader with an idea of how much can the proposed techniques be used in end-to-end train system for cross lingual tasks. 3)The authors comment on the quality of embeddings for languages such as Bengali, when studying the problem of alignment. Do the authors have any opinion if use of meta-embeddings i.e a weighted combination of say GloVe, word2vec and FastText can over come some of this limitations?Since each of these methods exploits a different feature of the training corpus, and there is rich literature showing the effectiveness of meta-embeddings, would this be something worth pursuing?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Empirical observations on the instability of aligning word vector spaces with GANs
Unsupervised bilingual dictionary induction (UBDI) is useful for unsupervised machine translation and for cross-lingual transfer of models into low-resource languages. One approach to UBDI is to align word vector spaces in different languages using Generative adversarial networks (GANs) with linear generators, achieving state-of-the-art performance for several language pairs. For some pairs, however, GAN-based induction is unstable or completely fails to align the vector spaces. We focus on cases where linear transformations provably exist, but the performance of GAN-based UBDI depends heavily on the model initialization. We show that the instability depends on the shape and density of the vector sets, but not on noise; it is the result of local optima, but neither over-parameterization nor changing the batch size or the learning rate consistently reduces instability. Nevertheless, we can stabilize GAN-based UBDI through best-of-N model selection, based on an unsupervised stopping criterion.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This paper is clear and well written, except for the occasional typos, eg. missing a bracket in the very first equation. The problem of learning transformations to align vector spaces across languages is extremely interesting and relevant. While the paper does a thorough job in explaining the metrics used and in detailing the procedure followed, I would have liked to see the following the details in the paper, 1)Number of unique word tokens in Bengali, Cebuano etc. While clicking on the reference link to MUSE provides these details, including the same in the paper, would make the writing wholesome. 2)While a thorough theoretical analysis is provided to explain the quality of the transformation/rotation matrix, there are no empirical results provided on any cross-lingual classification tasks. While not too important to the paper, such an analysis would provide the reader with an idea of how much can the proposed techniques be used in end-to-end train system for cross lingual tasks. 3)The authors comment on the quality of embeddings for languages such as Bengali, when studying the problem of alignment. Do the authors have any opinion if use of meta-embeddings i.e a weighted combination of say GloVe, word2vec and FastText can over come some of this limitations?Since each of these methods exploits a different feature of the training corpus, and there is rich literature showing the effectiveness of meta-embeddings, would this be something worth pursuing?
assistant
 This paper is clear and well written, except for the occasional typos, eg. missing a bracket in the very first equation. The problem of learning transformations to align vector spaces across languages is extremely interesting and relevant. While the paper does a thorough job in explaining the metrics used and in detailing the procedure followed, I would have liked to see the following the details in the paper, 1)Number of unique word tokens in Bengali, Cebuano etc. While clicking on the reference link to MUSE provides these details, including the same in the paper, would make the writing wholesome. 2)While a thorough theoretical analysis is provided to explain the quality of the transformation/rotation matrix, there are no empirical results provided on any cross-lingual classification tasks. While not too important to the paper, such an analysis would provide the reader with an idea of how much can the proposed techniques be used in end-to-end train system for cross lingual tasks. 3)The authors comment on the quality of embeddings for languages such as Bengali, when studying the problem of alignment. Do the authors have any opinion if use of meta-embeddings i.e a weighted combination of say GloVe, word2vec and FastText can over come some of this limitations?Since each of these methods exploits a different feature of the training corpus, and there is rich literature showing the effectiveness of meta-embeddings, would this be something worth pursuing?