Large pretrained language models (PLMs) are often domain- or task-adapted via finetuning or prompting. Finetuning requires modifying all of the parameters and having enough data to avoid overfitting while prompting requires no training and few examples but limits performance. Instead, we prepare PLMs for data- and parameter-efficient adaptation by learning to learn the difference between general and adapted PLMs. This difference is expressed in terms of model weights and sublayer structure through our proposed dynamic low-rank reparameterization and learned architecture controller. Experiments on few-shot dialogue completion, low-resource abstractive summarization, and multi-domain language modeling show improvements in adaptation time and performance over direct finetuning or preparation via domain-adaptive pretraining. Ablations show our task-adaptive reparameterization (TARP) and model search (TAMS) components individually improve on other parameter-efficient transfer like adapters and structure-learning methods like learned sparsification.

Hypothesis-level combination between multiple models can often yield gains in speech recognition. However, all models in the ensemble are usually restricted to use the same audio segmentation times. This paper proposes to generalise hypothesis-level combination, allowing the use of different audio segmentation times between the models, by splitting and re-joining the hypothesised N -best lists in time. A hypothesis tree method is also proposed to distribute hypothesis posteriors among the constituent words, to facilitate such splitting when per-word scores are not available. The approach is assessed on a Microsoft meeting transcription task, by performing combination between a streaming first-pass recognition and an offline second-pass recognition. The experimental results show that the proposed approach can yield gains when combining over different segmentation times. Furthermore, the results also show that a combination between a hybrid model and an end-to-end neural network model yields a greater improvement than a combination between two hybrid models.

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