Most contemporary multi-task learning methods assume linear models. This setting is considered shallow in the era of deep learning. In this paper, we present a new deep multi-task representation learning framework that learns cross-task sharing structure at every layer in a deep network. Our approach is based on generalising the matrix factorisation techniques explicitly or implicitly used by many conventional MTL algorithms to tensor factorisation, to realise automatic learning of end-to-end knowledge sharing in deep networks. This is in contrast to existing deep learning approaches that need a user-defined multi-task sharing strategy. Our approach applies to both homogeneous and heterogeneous MTL. Experiments demonstrate the efficacy of our deep multi-task representation learning in terms of both higher accuracy and fewer design choices.
Some studies consider heterogeneous MTL, where tasks may have different numbers of outputs . This differs from the previously discussed studies which implicitly assume that each task has a single output. Heterogeneous MTL typically uses neural networks with multiple sets of outputs and losses. E.g., @cite_6 proposes a shared-hidden-layer DNN model for multilingual speech processing, where each task corresponds to an individual language. @cite_3 uses a DNN to find facial landmarks (regression) as well as recognise facial attributes (classification); while @cite_2 proposes a DNN for query classification and information retrieval (ranking for web search). A key commonality of these studies is that they all require a user-defined parameter sharing strategy. A typical design pattern is to use shared layers (same parameters) for lower layers of the DNN and then split (independent parameters) for the top layers. However, there is no systematic way to make such design choices, so researchers usually rely on trial-and-error, further complicating the already somewhat dark art of DNN design. In contrast, our method learns where and how much to share representation parameters across the tasks, hence significantly reducing the space of DNN design choices.
@cite_3 @cite_6 @cite_2
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We propose a novel approach to addressing the adaptation effectiveness issue in parameter adaptation for deep neural network (DNN) based acoustic models for automatic speech recognition by adding one or more small auxiliary output layers modeling broad acoustic units, such as mono-phones or tied-state (often called senone) clusters. In scenarios with a limited amount of available adaptation data, most senones are usually rarely seen or not observed, and consequently the ability to model them in a new condition is often not fully exploited. With the original senone classification task as the primary task, and adding auxiliary mono-phone senone-cluster classification as the secondary tasks, multi-task learning (MTL) is employed to adapt the DNN parameters. With the proposed MTL adaptation framework, we improve the learning ability of the original DNN structure, then enlarge the coverage of the acoustic space to deal with the unseen senone problem, and thus enhance the discrimination power of the adapted DNN models. Experimental results on the 20,000-word open vocabulary WSJ task demonstrate that the proposed framework consistently outperforms the conventional linear hidden layer adaptation schemes without MTL by providing 3.2 relative word error rate reduction (WERR) with only 1 single adaptation utterance, and 10.7 WERR with 40 adaptation utterances against the un-adapted DNN models. We propose a multitask learning (MTL) approach to improve low-resource automatic speech recognition using deep neural networks (DNNs) without requiring additional language resources. We first demonstrate that the performance of the phone models of a single low-resource language can be improved by training its grapheme models in parallel under the MTL framework. If multiple low-resource languages are trained together, we investigate learning a set of universal phones (UPS) as an additional task again in the MTL framework to improve the performance of the phone models of all the involved languages. In both cases, the heuristic guideline is to select a task that may exploit extra information from the training data of the primary task(s). In the first method, the extra information is the phone-to-grapheme mappings, whereas in the second method, the UPS helps to implicitly map the phones of the multiple languages among each other. In a series of experiments using three low-resource South African languages in the Lwazi corpus, the proposed MTL methods obtain significant word recognition gains when compared with single-task learning (STL) of the corresponding DNNs or ROVER that combines results from several STL-trained DNNs. Multi-task learning (MTL) allows deep neural networks to learn from related tasks by sharing parameters with other networks. In practice, however, MTL involves searching an enormous space of possible parameter sharing architectures to find (a) the layers or subspaces that benefit from sharing, (b) the appropriate amount of sharing, and (c) the appropriate relative weights of the different task losses. Recent work has addressed each of the above problems in isolation. In this work we present an approach that learns a latent multi-task architecture that jointly addresses (a)--(c). We present experiments on synthetic data and data from OntoNotes 5.0, including four different tasks and seven different domains. Our extension consistently outperforms previous approaches to learning latent architectures for multi-task problems and achieves up to 15 average error reductions over common approaches to MTL. Multi-task learning (MTL) allows deep neural networks to learn from related tasks by sharing parameters with other networks. In practice, however, MTL involves searching an enormous space of possible parameter sharing architectures to find (a) the layers or subspaces that benefit from sharing, (b) the appropriate amount of sharing, and (c) the appropriate relative weights of the different task losses. Recent work has addressed each of the above problems in isolation. In this work we present an approach that learns a latent multi-task architecture that jointly addresses (a)–(c). We present experiments on synthetic data and data from OntoNotes 5.0, including four different tasks and seven different domains. Our extension consistently outperforms previous approaches to learning latent architectures for multi-task problems and achieves up to 15 average error reductions over common approaches to MTL.