[PDF] Top 20 A Weakly Supervised Learning Approach for Spoken Language Understanding
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A Weakly Supervised Learning Approach for Spoken Language Understanding
... natural language corpus was collected through a specific website which simulated a dialog ...a spoken language corpus was collected through the deployment of a pre- liminary version of ... See full document
9
Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents
... triangular learning rates ...different learning rates across network layers. Here, we use a lower learning rate for the bottom layer to avoid large updates in the transferred knowledge of the lower ... See full document
8
Weakly supervised learning of allomorphy
... natural language processing, mor- phological segmentation is a well-researched and established problem (Goldsmith (2001), Creutz and Lagus (2005), Poon et ... See full document
11
Dual Supervised Learning for Natural Language Understanding and Generation
... natural language processing ...natural language in- teractions. The recent advance of deep learning has inspired many applications of neural dialogue systems (Wen et ...guage understanding ... See full document
6
Graph Based Semi Supervised Learning for Natural Language Understanding
... semi-supervised learning models as well as their inductive variants for ...first approach to ap- ply text based graph structure for an SSL of ...semi-supervised learning can reduce the ... See full document
8
A Graph based Cross lingual Projection Approach for Weakly Supervised Relation Extraction
... The most crucial factor in the success of graph- based learning approaches is how to construct a graph that is appropriate for the target task. Das and Petrov (Das and Petrov, 2011) proposed a graph- based ... See full document
6
Active Learning in Noisy Conditions for Spoken Language Understanding
... Spoken language understanding (SLU) is currently an emerging field in the intersection of speech processing and natural language processing (Tur and De Mori, ...natural language ... See full document
10
Recognizing Explicit and Implicit Hate Speech Using a Weakly Supervised Two path Bootstrapping Approach
... The error of our model comes from semantic drift in bootstrapping learning, which partially results from the complexity and dynamics of language. Specifically, we found dynamic word sense of slurs and ... See full document
9
Structured Learning for Context aware Spoken Language Understanding of Robotic Commands
... data-driven approach that integrates an explicit semantic representation with linguistic generalization induced through ma- chine ...machine learning depends on such percep- tual information, thus inducing ... See full document
10
FANDA: A Novel Approach to Perform Follow-Up Query Analysis
... Natural Language Interfaces to Databases (NLIDB) has attracted considerable ...natural language instead of SQL-like query ...with weakly supervised max-margin ... See full document
8
Three phase training to address data sparsity in Neural Machine Translation
... across language pairs, thereby pro- viding high quality translations de- spite the lack of large parallel cor- ...integrated approach which combines weakly supervised and semi- ... See full document
10
Learning Representations for Weakly Supervised Natural Language Processing Tasks
... representation-learning approach to domain adaptation is an instance of semi-supervised ...semi- supervised techniques, we concentrate on a particularly simple task decomposition: un- ... See full document
36
Weakly Supervised Learning of Presupposition Relations between Verbs
... natural language is that significant portions of content conveyed in a mes- sage may not be overtly ...natural language processing applications, such as information ex- traction, text understanding, ... See full document
6
Weakly Supervised Natural Language Learning Without Redundant Views
... underlying learning algorithm to train a coreference classifier, simply because (1) it provides a generative model assumed by EM and hence facilitates comparison between different approaches and (2) it is more ... See full document
8
Weak Supervision for Learning Discourse Structure
... structure learning problem to predict- ing edges or attachments between discourse unit (DU) pairs in the dependency ...a supervised deep learning algorithm to pre- dict attachments on the STAC ... See full document
10
Language Models as Representations for Weakly Supervised NLP Tasks
... For each representation, we measured the accuracy of the POS tagger on the biomedical test text. Ta- ble 2 shows the results for the best variation of each kind of model — 20 layers for the PL-MRF, 7 lay- ers for the ... See full document
10
On the Feasibility of Automatically Describing n dimensional Objects
... using supervised classification, the task of de- bugging and improving their classifiers at times in- volves repeated steps of training with different pa- ...Natural Language descriptions for the error ... See full document
6
Deep Residual Learning for Weakly Supervised Relation Extraction
... residual learning (ResNet) (He et ...ual learning on noisy natural language pro- cessing tasks is still not well ...ual learning, and investigate its impacts on the task of distantly ... See full document
5
Structure and Intonation in Spoken Language Understanding
... The combinatory theory thus offers a way to derive such intonational phrases, using only the independently motivated rules of combinatory grammar, entirely under the control of appropria[r] ... See full document
8
Weakly Supervised Learning for Hedge Classification in Scientific Literature
... the weakly supervised learning models are significantly more accurate than the baseline according to a binomial sign test (p < ...our learning model reach approximately ... See full document
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