In this work, we propose to study the performance of a model trained with a sentence embedding regression loss component for the Automated Audio Captioning task. This task aims to build systems that can describe audio content with a single sentence written in natural language. Most systems are trained with the standard Cross-Entropy loss, which does not take into account the semantic closeness of the sentence. We found that adding a sentence embedding loss term reduces overfitting, but also increased SPIDEr from 0.397 to 0.418 in our first setting on the AudioCaps corpus. When we increased the weight decay value, we found our model to be much closer to the current state-of-the-art methods, with a SPIDEr score up to 0.444 compared to a 0.475 score. Moreover, this model uses eight times less trainable parameters. In this training setting, the sentence embedding loss has no more impact on the model performance.

翻译：本文提出在自动音频描述任务中研究引入句子嵌入回归损失分量训练的模型性能。该任务旨在构建能够用自然语言单句描述音频内容的系统。大多数系统采用标准交叉熵损失函数进行训练，该损失函数未考虑句子语义相似性。研究发现，在AudioCaps语料库的首个实验设置中，增加句子嵌入损失项既能减少过拟合，又将SPIDEr评分从0.397提升至0.418。当增大权重衰减值时，我们的模型与当前最先进方法的差距显著缩小，SPIDEr评分达0.444（对比基准0.475）。值得注意的是，该模型可训练参数数量仅为前者的八分之一。在此训练配置下，句子嵌入损失对模型性能不再产生额外影响。