How can we measure the generalization of models to a variety of unseen tasks when provided with their language instructions? To facilitate progress in this goal, we introduce NATURAL-INSTRUCTIONS v2 , a benchmark of 1,600+ diverse language tasks and their expertwritten instructions. It covers 70+ distinct task types, such as tagging, in-filling, and rewriting. These tasks are collected with contributions of NLP practitioners in the community and through an iterative peer review process to ensure their quality. With this large and diverse collection of tasks, we are able to rigorously benchmark cross-task generalization of models-training on a subset of tasks and evaluating on the remaining unseen ones. For instance, we quantify generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances, and model sizes. Based on these insights, we introduce Tk-INSTRUCT, an encoder-decoder Transformer that is trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples) which outperforms existing larger models on our benchmark. We hope this benchmark facilitates future progress toward more general-purpose language understanding models. 1

No abstract

No abstract

No abstract

Recently, Convolutional Neural Networks (CNN)-based Generative Adversarial Networks (GANs) are used for Whisper-to-Normal Speech (i.e., WHSP2SPCH) conversion task. These CNN-based GANs are significantly difficult to train in terms of computational complexity. Goal of the generator in GAN is to map the features of the whispered speech to that of the normal speech efficiently. To improve the performance, we need to either tune the cost functions by changing hyperparameters associated with it or to make the generator more complex by adding more layers to the model. However, more complex architectures are prone to overfitting. Both solutions are time-consuming and computationally expensive. Hence, in this paper, we propose Inception-based GAN architecture (i.e., Inception-GAN). Our proposed architecture is quite stable and computationally less expensive while training. The proposed Inception-GAN outperforms existing CNN-based GAN architectures (CNN-GAN). Objective and subjective results are carried out using the proposed architectures on statistically meaningful whispered TIMIT (wTIMIT) corpus. For a speakerspecific evaluations, Inception-GAN shows 8.9% and 6.2% better perfomance objectively compared to the CNN-based GAN for male and female speaker, respectively.