Semantic embeddings play a crucial role in natural language-based information
retrieval. Embedding models represent words and contexts as vectors whose
spatial configuration is derived from the distribution of words in large text
corpora. While such representations are generally very powerful, they might
fail to account for fine-grained domain-specific nuances. In this article, we
investigate this uncertainty for the domain of characterizations of expressive
piano performance. Using a music research dataset of free text performance
characterizations and a follow-up study sorting the annotations into clusters,
we derive a ground truth for a domain-specific semantic similarity structure.
We test five embedding models and their similarity structure for correspondence
with the ground truth. We further assess the effects of contextualizing
prompts, hubness reduction, cross-modal similarity, and k-means clustering. The
quality of embedding models shows great variability with respect to this task;
more general models perform better than domain-adapted ones and the best model
configurations reach human-level agreement.

使用五个嵌入模型和他们的相似性结构，我们研究了表达钢琴演奏特点的领域中的语义嵌入的不确定性，并评估了上下文提示、减少中心性、跨模态相似度和 k-means 聚类的效果。结果显示，针对这一任务的嵌入模型质量存在很大的变异性；更一般的模型表现优于领域适应模型，并且最佳模型配置达到与人类级别的一致性。

描述的是否是同一声音？一项对表现钢琴演奏的词嵌入空间的分析

Are we describing the same sound? An analysis of word embedding spaces  of expressive piano performance

Large pre-trained models can dramatically reduce the amount of task-specific
data required to solve a problem, but they often fail to capture
domain-specific nuances out of the box. The Web likely contains the information
necessary to excel on any specific application, but identifying the right data
a priori is challenging. This paper shows how to leverage recent advances in
NLP and multi-modal learning to augment a pre-trained model with search engine
retrieval. We propose to retrieve useful data from the Web at test time based
on test cases that the model is uncertain about. Different from existing
retrieval-augmented approaches, we then update the model to address this
underlying uncertainty. We demonstrate substantial improvements in zero-shot
performance, e.g. a remarkable increase of 15 percentage points in accuracy on
the Stanford Cars and Flowers datasets. We also present extensive experiments
that explore the impact of noisy retrieval and different learning strategies.

使用 NLP 和多模式学习，该论文介绍了如何通过搜索引擎检索来增强预训练模型，从而在测试时从互联网中检索到有用的数据，并更新模型以解决模型的不确定性，取得了零样本性能的显著改进。