YNU-HPCC at SemEval-2020 Task 10: Using a Multi-granularity Ordinal Classification of the BiLSTM Model for Emphasis Selection

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 1710–1715 Barcelona, Spain (Online), December 12, 2020. 1710 YNU-HPCC at SemEval-2020 Task 10: Using a Multi-granularity Ordinal Classification of the BiLSTM Model for Emphasis Selection Dawei Liao, Jin Wang and Xuejie Zhang School of Information Science and Engineering Yunnan University Kunming, China Contact:davidblackingongan@qq.com {wangjin, xjzhang}@ynu.edu.cn Abstract Written text emphasis in visual media is used to increase the comprehension of written text, to grab a viewer’s attention, and to convey the author’s intent.The task is choosing candidates for emphasis in short written text, to enable automated design assistance in authoring. As the author’s intent is unknown and only the input text is available, multiple emphasis selections are valid. In this study, we propose a multi-granularity ordinal classification method to address the problem of emphasis selection. Specifically, word embeddings are learned from the Embeddings from Language Model (ELMo) to extract feature vector representations. Then, the ordinal classifications are implemented on four different multi-granularities to approximate the continuous emphasized values. Comparative experiments were conducted to compare the model with the baseline, in which the problem is transformed to a label distribution problem.The code of this paper is availabled at: https://github.com/DavidInWuhanChina/SemEval-2020-Task10. 1 Introduction Short texts have a great impact on visual communication. They are usually designed to grab a viewer’s attention and convey a message efficiently. For text, word emphasis is used to capture the intent better, removing the ambiguity that may exist in some plain texts. Word emphasis can clarify or even change the meaning of a sentence by drawing attention to some specific information, and it can be done with colors, backgrounds, fonts, italics, or boldface. The purpose of this task is to design automatic methods for emphasis selection, i.e., choosing candidates for emphasis in short written text, to enable automated design assistance in authoring (Shirani et al., 2020). In the task definition, given a sequence of words or tokens, there is a subset of words that are good candidates to emphasize, and the word probability represents the degree of emphasis, which needs to be predicted. In a previous work, a rule-based approach is used (Widera et al., 1997). Later, Text-based features such as part-of-speech (POS), information content, position in the sentence and other information was adopted (Volker Strom, 2007). Some methods have been proposed for predicting emphasized words for expressive Text-To-Speech (TTS) based on a deep neural network (DNN) (Mass et al., 2018; Rosenberg et al., 2015). To address the multiple annotators problem, a majority voting ensemble has been used to transform the problem into single-label learning (Laws et al., 2011). Shirani (2019) suggest that the task should be transformed into label distribution learning (LDL). The main difference between such a method and previous works is that the label is not a single dispersed value, but rather a continuous label distribution. Therefore, the model can fit more accurately to a real label instead of having the distortion of transforming a probability into a single label. The essence of the problem is …

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