Teacher: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].
Teacher: Now, understand the problem? Solve this instance: Overall, this is a nice paper. Developing a unifying framework for these newer neural models is a worthwhile endeavor. However, it's unclear if the DRAGNN framework (in its current form) is a significant standalone contribution. The main idea is straightforward: use a transition system to unroll a computation graph. When you implement models in this way you can reuse code because modules can be mixed and matched. This is nice, but (in my opinion) is just good software engineering, not machine learning research. Moreover, there appears to be little incentive to use DRAGNN, as there are no 'free things' (benefits) that you get by using the framework. For example: - If you write your neuralnet in an automatic differentiation library (e.g., tensorflow or dynet) you get gradients for 'free'. - In the VW framework, there are efficiency tricks that 'the credit assignment compiler' provides for you, which would be tedious to implement on your own. There is also a variety of algorithms for training the model in a principled way (i.e., without exposure bias). I don't feel that my question about the limitations of the framework has been satisfactorily addressed. Let me ask it in a different way: Can you give me examples of a few models that I can't (nicely) express in the DRAGNN framework? What if I wanted to implement
Student:
en