To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation

Active learning (AL) techniques reduce labeling costs for training neural machine translation (NMT) models by selecting smaller representative subsets from unlabeled data for annotation. Diversity sampling techniques select heterogeneous instances, while uncertainty sampling methods select instances with the highest model uncertainty. Both approaches have limitations - diversity methods may extract varied but trivial examples, while uncertainty sampling can yield repetitive, uninformative instances. To bridge this gap, we propose HUDS, a hybrid AL strategy for domain adaptation in NMT that combines uncertainty and diversity for sentence selection. HUDS computes uncertainty scores for unlabeled sentences and subsequently stratifies them. It then clusters sentence embeddings within each stratum using k-MEANS and computes diversity scores by distance to the centroid. A weighted hybrid score that combines uncertainty and diversity is then used to select the top instances for annotation in each AL iteration. Experiments on multi-domain German-English datasets demonstrate the better performance of HUDS over other strong AL baselines. We analyze the sentence selection with HUDS and show that it prioritizes diverse instances having high model uncertainty for annotation in early AL iterations.

Towards Representative Subset Selection for Self-Supervised Speech Recognition

Self-supervised speech recognition models require considerable labeled training data for learning high-fidelity representations for Automatic Speech Recognition (ASR), which hinders their application to low-resource languages. We consider the task of identifying an optimal subset of training data to fine-tune self-supervised speech models for ASR. We make a surprising observation that active learning strategies for sampling harder-to-learn examples do not perform better than random subset selection for fine-tuning self-supervised ASR. We then present the COWERAGE algorithm for better subset selection in self-supervised ASR which is based on our finding that ensuring the coverage of examples based on training WER in the early training epochs leads to better generalization performance. Extensive experiments on the wav2vec 2.0 model and TIMIT dataset show the effectiveness of COWERAGE, with up to 27% absolute WER improvement over active learning methods. We also report the connection between training WER and the phonemic cover and demonstrate that our algorithm ensures inclusion of phonemically diverse examples.

FedPrune: Towards Inclusive Federated Learning

Federated learning (FL) is a distributed learning technique that trains a shared model over distributed data in a privacy-preserving manner. Unfortunately, FL's performance degrades when there is (i) variability in client characteristics in terms of computational and memory resources (system heterogeneity) and (ii) non-IID data distribution across clients (statistical heterogeneity). For example, slow clients get dropped in FL schemes, such as Federated Averaging (FedAvg), which not only limits overall learning but also biases results towards fast clients. We propose FedPrune; a system that tackles this challenge by pruning the global model for slow clients based on their device characteristics. By doing so, slow clients can train a small model quickly and participate in FL which increases test accuracy as well as fairness. By using insights from Central Limit Theorem, FedPrune incorporates a new aggregation technique that achieves robust performance over non-IID data. Experimental evaluation shows that Fed- Prune provides robust convergence and better fairness compared to Federated Averaging.