YNU-HPCC at SemEval-2020 Task 10: Using a Multi-granularity Ordinal Classification of the BiLSTM Model for Emphasis Selection
In this study, we propose a multi-granularity ordinal classification method to address the problem of emphasis selection. In detail, the word embedding is learned from Embeddings from Language Model (ELMO) to extract feature vector representation. Then, the ordinal classifica-tions are implemented on four different multi-granularities to approximate the continuous em-phasize values. Comparative experiments were conducted to compare the model with baseline in which the problem is transformed to label distribution problem.
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