[PDF] Top 20 Nonparametric Bayesian Models for Spoken Language Understanding
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Nonparametric Bayesian Models for Spoken Language Understanding
... in spoken language understanding using a nonparamet- ric Bayesian ...a nonparametric Bayesian model in- volving the generation of arbitrary natural lan- guage phrases, which ... See full document
9
Exploiting Non Local Features for Spoken Language Understanding
... Next, we compared the two trigger selection methods; mutual information (MI) and feature in- duction (FI). Table 2 shows the experimental re- sults of the comparison between MI and FI ap- proaches (with the local feature ... See full document
8
Re Ranking Models Based on Small Training Data for Spoken Language Understanding
... The kernels described in previous sections pro- vide a powerful technology for exploiting features of structured data. These kernels were originally designed for data annotated with syntactic parse trees. In ... See full document
10
Spoken Language Understanding: from Spoken Utterances to Semantic Structures
... Our main contribution is the implementation of a module working as junction between the two components of the joint models. The module takes as input the interpretations generated by the first model and convert ... See full document
148
On some recent advances on high dimensional Bayesian statistics
... in Bayesian statistics in high dimensional or nonparametric ...Markov models (HMM for ...a nonparametric point of view for understanding the behaviour of estimators computed from abc ... See full document
27
Practical Semantic Parsing for Spoken Language Understanding
... Overnight It contains sentences annotated with Lambda DCS (Liang, 2013). The sentences are di- vided into eight domains: calendar, blocks, hous- ing, restaurants, publications, recipes, socialnet- work, and basketball. ... See full document
8
Integrating Prosodics into a Language Model for Spoken Language Understanding of Thai
... A language model usually consists of a grammar which is applied to a sentence by utilizing a parsing algorithm to account for syntactic representation of the recognized string of ...the language model must ... See full document
10
Exploiting multiple hypotheses for Multilingual Spoken Language Understanding
... these models is that they can be learned from ...the language to express the se- mantics as much as ...discriminative models (like Conditional Random Fields (Hahn et ...generative models (such ... See full document
9
Data Augmentation with Atomic Templates for Spoken Language Understanding
... The models cannot con- trol which kinds of semantic meaning should be generated for ...The models cannot gen- erate data for new semantic representations which may contain out-of-vocabulary (OOV) ... See full document
7
A Model of Zero Shot Learning of Spoken Language Understanding
... word embeddings that are used to initialize word embedding parameters. For this, we use an En- glish Wikipedia dump as our unlabelled training corpus, which is a diverse broad-coverage corpus. It has been shown (Baroni ... See full document
6
A Weakly Supervised Learning Approach for Spoken Language Understanding
... from the input utterance the information needed to complete the query. Traditionally, there are mainly two mainstreams in the SLU researches: knowledge-based approaches, which are based on robust parsing or template ... See full document
9
Neural Lexicons for Slot Tagging in Spoken Language Understanding
... neural models in an industry set- ting. We focus on LSTM models since they have been shown to produce state-of-the-art results in many natural language ... See full document
7
Re Ranking Models for Spoken Language Understanding
... Third, the kernels which produce higher number of substructures, i.e. PTK and SK, improves the kernel less rich in terms of features, i.e. STK. For example, using split training and approach A, STK is improved by ... See full document
9
Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding
... discriminative models such as CRFs and sequence neural networks have been widely explored for spoken language ...these models use word-level semantic ... See full document
10
Combining Statistical and Knowledge Based Spoken Language Understanding in Conditional Models
... An important lesson we have learned from the previous experiment is that we should not think generatively when applying conditional models. While it is important to find cues that help identify the slots, there is ... See full document
8
Gemini: A Natural Language System for Spoken Language Understanding
... Gemini A Natural Language System for Spoken Language Understanding Gemini A N a t u r a l Language S y s t e m for Spoken Language Understanding* John Dowding, Jean Mark Gawron, Doug Appelt, John Bear[.] ... See full document
6
Corpus Development Activities at the Center for Spoken Language Understanding
... Corpus Development Activities at the Center for Spoken Language Understanding Corpus D e v e l o p m e n t Activities at the Center for Spoken Language U n d e r s t a n d i n g Ron Cole, Mike Noel, D[.] ... See full document
6
Active Learning in Noisy Conditions for Spoken Language Understanding
... In this paper, best known active learning methods applicable to sequence labeling tasks were evaluated in the field of SLU (Spoken Language Understanding) in real conditions of noise. The new method ... See full document
10
Robust, Finite State Parsing for Spoken Language Understanding
... Aside from infinite center-self-embedding, a regular grammar formalism like PROFER's can be used to define every pattern in natural language definable by a GLR parser... Figure 4: Finite[r] ... See full document
6
Learning to Create and Reuse Words in Open Vocabulary Neural Language Modeling
... ural language processing with many practical ap- plications (translation, speech recognition, spelling autocorrection, ...to language models with distributed representa- tions and unbounded ... See full document
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