This technical report describes the system participating to the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 Challenge, Task 6: automated audio captioning. Our submission focuses on solving two indeterminacy problems in automated audio captioning: word selection indeterminacy and sentence length indeterminacy. We simultaneously solve the main caption generation and sub indeterminacy problems by estimating keywords and sentence length through multi-task learning. We tested a simplified model of our submission using the development-testing dataset. Our model achieved 20.7 SPIDEr score where that of the baseline system was 5.4.

中文翻译：

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NTT DCASE2020 挑战任务 6 系统：带有关键字和句子长度估计的自动音频字幕

本技术报告描述了参与声学场景和事件检测和分类 (DCASE) 2020 挑战赛任务 6：自动音频字幕的系统。我们提交的重点是解决自动音频字幕中的两个不确定性问题：词选择不确定性和句子长度不确定性。我们通过多任务学习估计关键字和句子长度，同时解决主字幕生成和子不确定性问题。我们使用开发测试数据集测试了我们提交的简化模型。我们的模型获得了 20.7 的 SPIDER 分数，而基线系统的分数为 5.4。