Definition: In this task, you are given a paper review. Based on the review, your job is to identify language and generate "en" if the review is in English or generate "es" if the review is in Spanish. Note that URLs in the text have been replaced with [Link].
Input: This paper introduces UCCA as a target representation for semantic parsing and also describes a quite successful transition-based parser for inference into that representation. I liked this paper a lot. I believe there is a lot of value simply in the introduction of UCCA (not new, but I believe relatively new to this community), which has the potential to spark new thinking about semantic representations of text. I also think the model was well thought out. While the model itself was fairly derivative of existing transition-based schemes, the extensions the authors introduced to make the model applicable in this domain were reasonable and well-explained, at what I believe to be an appropriate level of detail. The empirical evaluation was pretty convincing -- the results were good, as compared to several credible baselines, and the authors demonstrated this performance in multiple domains. My biggest complaint about this paper is the lack of multilingual evaluation, especially given that the formalism being experimented with is exactly one that is supposed to be fairly universal. I'm reasonably sure multilingual UCCA corpora exist (in fact, I think the "20k leagues" corpus used in this paper is one such), so it would be good to see results in a language other than English. One minor point: in section 6, the authors refer to their model as "grammarless", which strikes me as not quite correct. It's true that the UCCA representation isn't derived from linguistic notions of syntax, but it still defines a way to construct a compositional abstract symbolic representation of text, which to me, is precisely a grammar. (This is clearly a quibble, and I don't know why it irked me enough that I feel compelled to address it, but it did.) Edited to add: Thanks to the authors for their response.
Output:
en