Exploring Temporal Context Knowledge for Real-time, Multi-lingual Conversational Search


Exploring Temporal Context Knowledge for Real-time, Multi-lingual Conversational Search – We present a novel method for understanding temporal ambiguity in the wild. The proposed model is a neural network trained to predict the current tense state of a language user’s speech, or a sequence of sentences. As the user’s speech becomes more and more important (i.e., more relevant to the current tense state), this is an opportunity for the user to improve his or her understanding of the language’s tense state. An automatic learning tool, we call Temporal Context Knowledge (TCK), is used to predict the next tense state of a user’s speech to achieve a more detailed understanding of the current tense state. Our model combines the temporal context knowledge from the user and the semantic content in his or her speech into the state-action tree. We build an automatic and robust neural network model to predict the current tense state of user’s speech using the knowledge extracted by our neural network. Experiments are conducted using the MIMI dataset and on two different languages. Results show that our model outperforms current state-action learning methods for predicting the current tense state of users by a large margin.

We present a neural optimization method for image based keyword detection. The proposed method uses feature extraction of input images as an optimization step to optimally optimize the distance of the image domain to ground truth. We develop a new supervised learning model for image segmentation and propose a novel two-stage method that simultaneously optimizes distance based on semantic distance and semantic distance in order to identify relevant features in the image domain. The proposed algorithm is fully supervised but trained on a large dataset of thousands of images and evaluated on a set of 1000 images. In addition to the fine-tuning phase, there are a number of evaluation conditions and a number of experiments with different objectives are included to show the quality of the proposed algorithm.

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Exploring Temporal Context Knowledge for Real-time, Multi-lingual Conversational Search

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  • A Survey of Sparse Spectral Analysis

    Learning a Latent Feature Model with Group Sparsity to Improve Keyword ExtractionWe present a neural optimization method for image based keyword detection. The proposed method uses feature extraction of input images as an optimization step to optimally optimize the distance of the image domain to ground truth. We develop a new supervised learning model for image segmentation and propose a novel two-stage method that simultaneously optimizes distance based on semantic distance and semantic distance in order to identify relevant features in the image domain. The proposed algorithm is fully supervised but trained on a large dataset of thousands of images and evaluated on a set of 1000 images. In addition to the fine-tuning phase, there are a number of evaluation conditions and a number of experiments with different objectives are included to show the quality of the proposed algorithm.


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