论文标题
NTT DCASE2020挑战任务6系统：具有关键字和句子长度估计的自动化音频字幕
The NTT DCASE2020 Challenge Task 6 system: Automated Audio Captioning with Keywords and Sentence Length Estimation
论文作者
论文摘要
该技术报告描述了参与声学场景和事件的检测和分类的系统（DCASE）2020挑战，任务6：自动化音频字幕。我们的提交重点是在自动化音频字幕中解决两个不确定性问题：单词选择不确定性和句子长度不确定性。我们通过通过多任务学习估算关键字和句子长度来同时解决主要的标题生成和子不确定性问题。我们使用开发测试数据集测试了提交的简化模型。我们的模型达到20.7蜘蛛分数，基线系统的分数为5.4。
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.
