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arXiv · 1608.06378

Towards Machine Comprehension of Spoken Content: Initial TOEFL Listening Comprehension Test by Machine

Bo-Hsiang Tseng·Sheng-Syun Shen·Hung-Yi Lee·Lin-Shan Lee
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Abstract

Multimedia or spoken content presents more attractive information than plain text content, but it's more difficult to display on a screen and be selected by a user. As a result, accessing large collections of the former is much more difficult and time-consuming than the latter for humans. It's highly attractive to develop a machine which can automatically understand spoken content and summarize the key information for humans to browse over. In this endeavor, we propose a new task of machine comprehension of spoken content. We define the initial goal as the listening comprehension test of TOEFL, a challenging academic English examination for English learners whose native language is not English. We further propose an Attention-based Multi-hop Recurrent Neural Network (AMRNN) architecture for this task, achieving encouraging results in the initial tests. Initial results also have shown that word-level attention is probably more robust than sentence-level attention for this task with ASR errors.

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Bo-Hsiang Tseng, Sheng-Syun Shen, Hung-Yi Lee, Lin-Shan Lee. 2016-08-23. Towards Machine Comprehension of Spoken Content: Initial TOEFL Listening Comprehension Test by Machine. https://arxiv.org/abs/1608.06378

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