Over-dependence on domain ontology and lack of knowledge sharing across domains are two practical and yet less studied problems of dialogue state tracking. Existing approaches generally fall short in tracking unknown slot values during inference and often have difficulties in adapting to new domains. In this paper, we propose a Transferable Dialogue State Generator (TRADE) that generates dialogue states from utterances using a copy mechanism, facilitating knowledge transfer when predicting (domain, slot, value) triplets not encountered during training. Our model is composed of an utterance encoder, a slot gate, and a state generator, which are shared across domains. Empirical results demonstrate that TRADE achieves state-of-the-art joint goal accuracy of 48.62 for the five domains of MultiWOZ, a human-human dialogue dataset. In addition, we show its transferring ability by simulating zero-shot and few-shot dialogue state tracking for unseen domains. TRADE achieves 60.58 joint goal accuracy in one of the zero-shot domains, and is able to adapt to few-shot cases without forgetting already trained domains.
Different components of dialogue systems have previously been used for zero-shot application, e.g., intention classifiers @cite_30 , slot-filling @cite_22 , and dialogue policy @cite_35 . For language generation, propose single encoder-decoder models for zero-shot machine translation, and propose cross-domain zero-shot dialogue generation using action matching. Moreover, few-shot learning in natural language applications has been applied in semantic parsing @cite_16 , machine translation @cite_34 , and text classification @cite_7 with meta-learning approaches @cite_17 @cite_20 . These tasks usually have multiple tasks to perform fast adaptation, instead in our case the number of existing domains are limited. Lastly, several approaches have been proposed for continual learning in the machine learning community @cite_4 @cite_12 @cite_23 @cite_25 @cite_38 , especially in image recognition tasks @cite_9 @cite_36 . The applications within NLP has been comparatively limited, e.g., for opinion mining, for document classification, and for hybrid code networks @cite_39 .
@cite_30 @cite_35 @cite_38 @cite_4 @cite_22 @cite_7 @cite_25 @cite_36 @cite_9 @cite_34 @cite_39 @cite_23 @cite_16 @cite_12 @cite_20 @cite_17
2891844856 2141350700 2516449915 2398118205
Transferring representations from large supervised tasks to downstream tasks has shown promising results in AI fields such as Computer Vision and Natural Language Processing (NLP). In parallel, the recent progress in Machine Translation (MT) has enabled one to train multilingual Neural MT (NMT) systems that can translate between multiple languages and are also capable of performing zero-shot translation. However, little attention has been paid to leveraging representations learned by a multilingual NMT system to enable zero-shot multilinguality in other NLP tasks. In this paper, we demonstrate a simple framework, a multilingual Encoder-Classifier, for cross-lingual transfer learning by reusing the encoder from a multilingual NMT system and stitching it with a task-specific classifier component. Our proposed model achieves significant improvements in the English setup on three benchmark tasks - Amazon Reviews, SST and SNLI. Further, our system can perform classification in a new language for which no classification data was seen during training, showing that zero-shot classification is possible and remarkably competitive. In order to understand the underlying factors contributing to this finding, we conducted a series of analyses on the effect of the shared vocabulary, the training data type for NMT, classifier complexity, encoder representation power, and model generalization on zero-shot performance. Our results provide strong evidence that the representations learned from multilingual NMT systems are widely applicable across languages and tasks. Most existing zero-shot learning approaches exploit transfer learning via an intermediate semantic representation shared between an annotated auxiliary dataset and a target dataset with different classes and no annotation. A projection from a low-level feature space to the semantic representation space is learned from the auxiliary dataset and applied without adaptation to the target dataset. In this paper we identify two inherent limitations with these approaches. First, due to having disjoint and potentially unrelated classes, the projection functions learned from the auxiliary dataset domain are biased when applied directly to the target dataset domain. We call this problem the projection domain shift problem and propose a novel framework, transductive multi-view embedding , to solve it. The second limitation is the prototype sparsity problem which refers to the fact that for each target class, only a single prototype is available for zero-shot learning given a semantic representation. To overcome this problem, a novel heterogeneous multi-view hypergraph label propagation method is formulated for zero-shot learning in the transductive embedding space. It effectively exploits the complementary information offered by different semantic representations and takes advantage of the manifold structures of multiple representation spaces in a coherent manner. We demonstrate through extensive experiments that the proposed approach (1) rectifies the projection shift between the auxiliary and target domains, (2) exploits the complementarity of multiple semantic representations, (3) significantly outperforms existing methods for both zero-shot and N-shot recognition on three image and video benchmark datasets, and (4) enables novel cross-view annotation tasks. With the sustaining bloom of multimedia data, Zero-shot Learning (ZSL) techniques have attracted much attention in recent years for its ability to train learning models that can handle “unseen” categories. Existing ZSL algorithms mainly take advantages of attribute-based semantic space and only focus on static image data. Besides, most ZSL studies merely consider the semantic embedded labels and fail to address domain shift problem. In this paper, we purpose a deep two-output model for video ZSL and action recognition tasks by computing both spatial and temporal features from video contents through distinct Convolutional Neural Networks (CNNs) and training a Multi-layer Perceptron (MLP) upon extracted features to map videos to semantic embedding word vectors. Moreover, we introduce a domain adaptation strategy named “ConSSEV” — by combining outputs from two distinct output layers of our MLP to improve the results of zero-shot learning. Our experiments on UCF101 dataset demonstrate the purposed model has more advantages associated with more complex video embedding schemes, and outperforms the state-of-the-art zero-shot learning techniques. State-of-the-art methods for zero-shot visual recognition formulate learning as a joint embedding problem of images and side information. In these formulations the current best complement to visual features are attributes: manuallyencoded vectors describing shared characteristics among categories. Despite good performance, attributes have limitations: (1) finer-grained recognition requires commensurately more attributes, and (2) attributes do not provide a natural language interface. We propose to overcome these limitations by training neural language models from scratch, i.e. without pre-training and only consuming words and characters. Our proposed models train end-to-end to align with the fine-grained and category-specific content of images. Natural language provides a flexible and compact way of encoding only the salient visual aspects for distinguishing categories. By training on raw text, our model can do inference on raw text as well, providing humans a familiar mode both for annotation and retrieval. Our model achieves strong performance on zero-shot text-based image retrieval and significantly outperforms the attribute-based state-of-the-art for zero-shot classification on the Caltech-UCSD Birds 200-2011 dataset.