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Extending from limited domain to a new domain is crucial for Natural Language Generation in Dialogue, especially when there are sufficient annotated data in the source domain, but there is little labeled data in the target domain. This paper studies the performance and domain adaptation of two different Neural Network Language Generators in Spoken Dialogue Systems: a gating-based Recurrent Neural...
We propose a novel deep layer cascade (LC) method to improve the accuracy and speed of semantic segmentation. Unlike the conventional model cascade (MC) that is composed of multiple independent models, LC treats a single deep model as a cascade of several sub-models. Earlier sub-models are trained to handle easy and confident regions, and they progressively feed-forward harder regions to the next...
Process Oriented Training and Learning can be applied in two different approaches: (a) processes describing the methodology of training and learning as well as (b) processes describing the organizational context that need to be learned. This paper introduces the results of the EU project Learn PAd that developed prototypes of modelling tools enabling business processes for learning and training. Flexibility...
While semantic visual attributes have been shown useful for a variety of tasks, many attributes are difficult to model computationally. One of the reasons for this difficulty is that it is not clear where in an image the attribute lives. We propose to tackle this problem by involving humans more directly in the process of learning an attribute model. We ask humans to examine a set of images to determine...
We present a Recurrent Neural Network (RNN), namely an Echo State Network (ESN), that performs sentence comprehension and can be used for Human-Robot Interaction (HRI). The RNN is trained to map sentence structures to meanings (i.e. predicates). We have previously shown that this ESN is able to generalize to unknown sentence structures. Moreover, it is able to learn English, French or both at the...
The learner modeling is a cornerstone of the personalized interaction in any Technology Enhanced Learning (TEL). Based on ontology's, the development of the Semantic Web offers new opportunities and challenges in the design of a new generation of adaptive systems, It's in this context that is situated the scope of our research works, with the aim of integrating a new modeling vision and learner pattern...
In this paper, we introduce the weighting of topic models in mixture language model adaptation using n-grams of the topic models. Topic clusters are formed by using a hard-clustering method assigning one topic to one document based on the maximum number of words chosen from a topic for that document in Latent Dirichlet Allocation (LDA) analysis. The n-grams of the topic generated by hard-clustering...
In this paper, we introduce an unsupervised language model adaptation approach using latent Dirichlet allocation (LDA) and dynamic marginals: locally estimated (smoothed) unigram probabilities from in-domain text data. In LDA analysis, topic clusters are formed by using a hard-clustering method assigning one topic to one document based on the maximum number of words chosen from a topic for that document...
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