An argument is divided into two parts, the claim and the reason. To obtain a clearer conclusion, some additional explanation is required. In this task, the explanations are called warrants. This paper introduces a bi-directional long short term memory (Bi-LSTM) with an attention model to select a correct warrant from two to explain an argument. We address this question as a question-answering system. For each warrant, the model produces a probability that it is correct. Finally, the system chooses the highest correct probability as the answer. Ensemble learning is used to enhance the performance of the model. Among all of the participants, we ranked 15th on the test results.

Building a system to detect Chinese grammatical errors is a challenge for natural-language processing researchers. As Chinese learners are increasing, developing such a system can help them study Chinese more easily. This paper introduces a bi-directional long short-term memory (BiLSTM) - conditional random field (CRF) model to produce the sequences that indicate an error type for every position of a sentence, since we regard Chinese grammatical error diagnosis (CGED) as a sequence-labeling problem.