The paper introduces a simple and effective method for morphological paradigm completion in low-resource settings. The method uses a character-based seq2seq model trained on a mix of examples in two languages: a resource-poor language and a closely-related resource-rich language; each training example is annotated with a paradigm properties and a language ID. Thus, the model enables transfer learning across languages when the two languages share common characters and common paradigms. While the proposed multi-lingual solution is not novel (similar architectures have been explored in syntax, language modeling, and MT), the novelty of this paper is to apply the approach to morphology. Experimental results show substantial improvements over monolingual baselines, and include a very thorough analysis of the impact of language similarities on the quality of results. The paper is interesting, very clearly written, I think it’ll be a nice contribution to the conference program.   Detailed comments:   — My main question is why the proposed general multilingual methodology was limited to pairs of languages, rather than to sets of similar languages? For example, all Romance languages could be included in the training to improve Spanish paradigm completion, and all Slavic languages with Cyrillic script could be mixed to improve Ukrainian. It would be interesting to see the extension of the models from bi-lingual to multilingual settings.   — I think Arabic is not a fair (and fairly meaningless) baseline, given how different is its script and morphology from the target languages. A more interesting baseline would be, e.g., a language with a partially shared alphabet but a different typology. For example, a Slavic language with Latin script could be used as a baseline language for Romance languages. If Arabic is excluded, and if we consider a most distant language in the same the same family as a baseline, experimental results are still strong.   — A half-page discussion of contribution of Arabic as a regularizer also adds little to the paper; I’d just remove Arabic from all the experiments and would add a regularizer (which, according to footnote 5, works even better than adding Arabic as a transfer language).                — Related work is missing a line of work on “language-universal” RNN models that use basically the same approach: they learn shared parameters for inputs in multiple languages, and add a language tag to the input to mediate between languages. Related studies include a multilingual parser (Ammar et al., 2016), language models (Tsvetkov et al., 2016), and machine translation (Johnson et al., 2016 )  Minor:  — I don’t think that the claim is correct in line 144 that POS tags are easy to transfer across languages. Transfer of POS annotations is also a challenging task.    References:   Waleed              Ammar, George Mulcaire, Miguel Ballesteros, Chris Dyer, and Noah A. Smith. "Many languages, one parser.” TACL 2016.   Yulia Tsvetkov, Sunayana Sitaram, Manaal Faruqui, Guillaume Lample, Patrick Littell, David Mortensen, Alan W. Black, Lori Levin, and Chris Dyer. "Polyglot neural language models: A case study in cross-lingual phonetic representation learning.” NAACL 2016.  Melvin Johnson, Mike Schuster, Quoc V. Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat et al. "Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation." arXiv preprint arXiv:1611.04558 2016.  -- Response to author response:   Thanks for your response & I'm looking forward to reading the final version!
Output: en