Task: Language Identification
Input (Spanish, English): This paper proposes to use RNN and reinforcement learning for solving combinatorial optimization problems. The use of pointer network is interesting as it enables generalization to arbitrary input size. The proposed method also "fintunes" on test examples with active search to achieve better performance.  The proposed method is theoretically interesting as it shows that RNN and RL can be combined to solve combinatorial optimization problems and achieve comparable performance to traditional heuristic based algorithms.  However, the lack of complexity comparison against baselines make it impossible to tell whether the proposed method has any practical value. The matter is further complicated by the fact that the proposed method runs on GPU while baselines run on CPU: it is hard to even come up with a meaningful unit of complexity. Money spent on hardware and electricity per instance may be a viable option.  Further more, the performance comparisons should be taken with a grain of salt as traditional heuristic based algorithms can often give better performance if allowed more computation, which is not controlled across algorithms.
Output (English): en