Repeated Reading (re-read), which means to read a sentence twice to get a better understanding, has been applied to machine reading tasks. But there have not been rigorous evaluations showing its exact contribution to natural language processing. In this paper, we design four tasks, each representing a different class of NLP tasks: (1) part-of-speech tagging, (2) sentiment analysis, (3) semantic relation classification, (4) event extraction. We take a bidirectional LSTM-RNN architecture as standard model for these tasks. Based on the standard model, we add repeated reading mechanism to make the model better "understand" the current sentence by reading itself twice. We compare three different repeated reading architectures: (1) Multi-level attention (2) Deep BiL-STM (3) Multi-pass BiLSTM, enforcing apples-to-apples comparison as much as possible. Our goal is to understand better in what situation repeated reading mechanism can help NLP task, and which of the three repeated reading architectures is more appropriate to repeated reading. We find that repeated reading mechanism do improve performance on some tasks (sentiment analysis, semantic relation classification, event extraction) but not on others (POS tagging). We discuss how these differences may be caused in each of the tasks. Then we give some suggestions for researchers to follow when choosing whether to use repeated model and which repeated model to use when faced with a new task. Our results thus shed light on the usage of repeated reading in NLP tasks.
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