Automated speaking assessment (ASA) typically involves automatic speech
recognition (ASR) and hand-crafted feature extraction from the ASR transcript
of a learner's speech. Recently, self-supervised learning (SSL) has shown
stellar performance compared to traditional methods. However, SSL-based ASA
systems are faced with at least three data-related challenges: limited
annotated data, uneven distribution of learner proficiency levels and
non-uniform score intervals between different CEFR proficiency levels. To
address these challenges, we explore the use of two novel modeling strategies:
metric-based classification and loss reweighting, leveraging distinct SSL-based
embedding features. Extensive experimental results on the ICNALE benchmark
dataset suggest that our approach can outperform existing strong baselines by a
sizable margin, achieving a significant improvement of more than 10% in CEFR
prediction accuracy.

使用自监督学习的方法，基于测量的分类和损失重新加权的两种新模型策略，提高了自动口语评估系统的学习者语言能力预测准确性。

解决数据稀缺和分布失衡问题的有效自动化口语评估方法

An Effective Automated Speaking Assessment Approach to Mitigating Data  Scarcity and Imbalanced Distribution

Humans have a remarkable ability to quickly and effectively learn new
concepts in a continuous manner without forgetting old knowledge. Though deep
learning has made tremendous successes on various computer vision tasks, it
faces challenges for achieving such human-level intelligence. In this paper, we
define a new problem called continual few-shot learning, in which tasks arrive
sequentially and each task is associated with a few training samples. We
propose Continual Meta-Learner (CML) to solve this problem. CML integrates
metric-based classification and a memory-based mechanism along with adversarial
learning into a meta-learning framework, which leads to the desirable
properties: 1) it can quickly and effectively learn to handle a new task; 2) it
overcomes catastrophic forgetting; 3) it is model-agnostic. We conduct
extensive experiments on two image datasets, MiniImageNet and CIFAR100.
Experimental results show that CML delivers state-of-the-art performance in
terms of classification accuracy on few-shot learning tasks without
catastrophic forgetting.

本文定义了一个新问题：不断出现的少样本学习；提出了 Continual Meta-Learner 来解决此问题，并在 MiniImageNet 和 CIFAR100 两个图像数据集上进行了广泛实验，表明 CML 在少量样本学习任务的分类准确性方面实现了现有最先进水平并避免了灾难性遗忘。