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].

Ex Input:
This paper introduces a new approach to semantic parsing in which the model is equipped with a neural sequence to sequence (seq2seq) model (referred to as the “programmer”) which encodes a natural language question and produces a program. The programmer is also equipped with a ‘key variable’ memory component which stores (a) entities in the questions (b) values of intermediate variables formed during execution of intermediate programs. These variables are referred to further build the program.                    The model is also equipped with certain discrete operations (such as argmax or 'hop to next edges in a KB'). A separate component ("interpreter/computer") executes these operations and stores intermediate values (as explained before). Since the ‘programmer' is inherently a seq2seq model, the "interpreter/computer” also acts as a syntax/type checker only allowing the decoder to generate valid tokens. For example, the second argument to the “hop” operation has to be a KB predicate. Finally the model is trained with weak supervision and directly optimizes the metric which is used to evaluate the performance (F score). Because of the discrete operations and the non differentiable reward functions, the model is trained with policy gradients (REINFORCE). Since gradients obtained through REINFORCE have high variance, it is common to first pretrain the model with a max-likelihood objective or find some good sequences of actions trained through some auxiliary objective. This paper takes a latter approach in which it finds good sequences via an iterative maximum likelihood approach. The results and discussion sections are presented in a very nice way and the model achieves SOTA results on the WebQuestions dataset when compared to other weakly supervised model.  The paper is written clearly and is very easy to follow.  This paper presents a new and exciting direction and there is scope for a lot of future research in this direction. I would definitely love to see this presented in the conference.  Questions for the authors (important ones first)  1. Another alternative way of training the model would be to bootstrap the parameters (\theta) from the iterative ML method instead of adding pseudo gold programs in the beam (Line 510 would be deleted). Did you try that and if so why do you think it didn’t work? 2. What was the baseline model in REINFORCE. Did you have a separate network which predicts the value function. This must be discussed in the paper in detail. 3. Were there programs which required multiple hop operations? Or were they limited to single hops. If there were, can you provide an example? (I will understand if you are bound by word limit of the response) 4. Can you give an example where the filter operation would be used? 5. I did not follow the motivation behind replacing the entities in the question with special ENT symbol  Minor comments: Line 161 describe -> describing Line 318 decoder reads ‘)’ -> decoder generates ‘)'

Ex Output:
en


Ex Input:
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!

Ex Output:
en


Ex Input:
No concuerdo con lo que está escrito en el segundo parágrafo de la introducción. Una solución para un problema NP completo, por ejemplo el cajero viajante, puede ser programada de forma simple... el problema radica en que la respuesta del programa puede tardar mucho. Para contornar esto se buscan heurísticas o meta-heurísticas que tal vez no encuentren la respuesta ideal pero si un buena respuesta.  En la página 3, observe que Pa no es un elemento de A y si un subconjunto de A o más precisamente un subconjunto de A-{a}. En ese mismo parágrafo habla de "dos tipos de restricciones" pero la segunda no está explícitamente colocada. Al final del parágrafo sería interesante decir que "o sea, ai pertenece a Paj". La frase está muy larga: 16 líneas!  No queda claro en las figuras 1 y 2 la restricción de que "la suma de la cantidad de tipo de recurso k utilizado en el período t, Rk(t), no debe exceder de Rk".  En la formulación matemática  del RCPSP no está claro que significaría por ejemplo que Fj tome el valor 5 y el significado de pj y de A(t).  Faltó una comparación estadística de cuan buena es la calidad de la solución encontrada con el algoritmo propuesto de otras soluciones o al menos una comparación que muestre que tan distante está la solución encontrada de la ideal.  Las referencias parecen no tener un orden lógico (alfabético o orden de citación).

Ex Output:

es