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[PDF] Top 20 Re Ranking Models Based on Small Training Data for Spoken Language Understanding

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Re Ranking Models Based on Small Training Data for Spoken Language Understanding

Re Ranking Models Based on Small Training Data for Spoken Language Understanding

... ing re-ranking, where the basic kernel was a tree kernel instead of ...to re-rank different candidates of the same hypothesis for machine ...parse re-ranking there is only one best hy- ... See full document

10

Combining Statistical and Knowledge Based Spoken Language Understanding in Conditional Models

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

Data Augmentation by Data Noising for Open vocabulary Slots in Spoken Language Understanding

Data Augmentation by Data Noising for Open vocabulary Slots in Spoken Language Understanding

... in Spoken Lan- guage Understanding (SLU) is dealing with ‘open-vocabulary’ ...els based on neural network were proposed, but it is still difficult to recognize the slots of unknown words or ... See full document

6

Cross lingual Transfer Learning with Data Selection for Large Scale Spoken Language Understanding

Cross lingual Transfer Learning with Data Selection for Large Scale Spoken Language Understanding

... Spoken Language Understanding (SLU) plays an important role in spoken language technology and is typically divided into two sub-tasks: Intent Clas- sification (IC) and Slot Filling ... See full document

6

Practical Semantic Parsing for Spoken Language Understanding

Practical Semantic Parsing for Spoken Language Understanding

... Q&A data set for our domain adap- tation scenario is the Overnight data set (Wang et ...relatively small. We also ex- periment with a larger semantic parsing data set (NLmaps; Lawrence and ... See full document

8

Data Augmentation with Atomic Templates for Spoken Language Understanding

Data Augmentation with Atomic Templates for Spoken Language Understanding

... generate data for new semantic ...The models cannot con- trol which kinds of semantic meaning should be generated for ...The models cannot gen- erate data for new semantic representations ... See full document

7

Selecting Machine Translated Data for Quick Bootstrapping of a Natural Language Understanding System

Selecting Machine Translated Data for Quick Bootstrapping of a Natural Language Understanding System

... addition, training on both MT and grammar-generated data improves performance over training solely on either one of the datasets; the improvement of the joined ap- proach is particularly large over ... See full document

8

Nonparametric Bayesian Models for Spoken Language Understanding

Nonparametric Bayesian Models for Spoken Language Understanding

... generative models is that prior knowledge can be integrated in an intuitive way (Raymond et ...less training data compared with discriminative models trained completely from scratch (Komatani ... See full document

9

Re Ranking Models for Spoken Language Understanding

Re Ranking Models for Spoken Language Understanding

... Spoken Language Understanding aims at mapping a natural language spoken sen- tence into a semantic ...changes based on the ...both models are used: a gen- erative model ... See full document

9

Spoken Language Understanding: from Spoken Utterances to Semantic Structures

Spoken Language Understanding: from Spoken Utterances to Semantic Structures

... the re-ranking model ...to re-rank hypotheses generated by any kind of SLU model. Re-ranking models described so far were based only on the SFST model, ...model. ... See full document

148

Integrating Prosodics into a Language Model for Spoken Language Understanding of Thai

Integrating Prosodics into a Language Model for Spoken Language Understanding of Thai

... The criteria for labeling a parse with the above strength features using the acoustic information are now described. The criteria establish the correspondence between the phonological (strength dynamics) and the ... See full document

10

Integrating Syntax and Semantics into Spoken Language Understanding

Integrating Syntax and Semantics into Spoken Language Understanding

... Integrating Syntax and Semantics into Spoken Language Understanding I n t e g r a t i n g S y n t a x a n d S e m a n t i c s i n t o S p o k e n L a n g u a g e U n d e r s t a n d i n g 1 Lynette Hi[.] ... See full document

6

Sampling Informative Training Data for RNN Language Models

Sampling Informative Training Data for RNN Language Models

... selecting training data for sentence-level RNN language ...n-gram language models’ rapid training and query time, which often requires just a single pass over the training ... See full document

5

GEMINI: A Natural Language System for Spoken Language Understanding

GEMINI: A Natural Language System for Spoken Language Understanding

... The Gemini kernel consists of a set of compilers to interpret the high-level languages in which the lexicon and syntactic and semantic grammar rules are written, as well as the parser, s[r] ... See full document

8

32nd Annual Meeting of the Association for Computational Linguistics

32nd Annual Meeting of the Association for Computational Linguistics

... MONDAY, 27 JUNE: Tutorials Computational Phonology Steven Bird Spoken Language Understanding Systems Stephanie Seneff Text Analysis Tools in Spoken-Language Processing Richard Sproat & M[r] ... See full document

14

The Karlsruhe Institute of Technology Translation Systems for the WMT 2012

The Karlsruhe Institute of Technology Translation Systems for the WMT 2012

... and language model ...the language model, source side context would also be valuable for the decoder when searching for the best translation ...source language context available we use a bilingual ... See full document

7

ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re Ranking

ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re Ranking

... Natural Language Processing group at Ari- zona State University for the TextGraphs 2019 Shared ...art language models and explore dataset prepa- ration ...iterative re- ranking ... See full document

7

Chart Parsing of Stochastic Spoken Language Models

Chart Parsing of Stochastic Spoken Language Models

... Chart Parsing of Stochastic Spoken Language Models C h a r t P a r s i n g o f S t o c h a s t i c S p o k e n L a n g u a g e M o d e l s C h a r l e s H e m p h i l l a n d J o s e p h P i c o n e T[.] ... See full document

6

Spoken Language Understanding for Personal Computers

Spoken Language Understanding for Personal Computers

... SPOKEN LANGUAGE UNDERSTANDING FOR PERSONAL COMPUTERS S P O K E N L A N G U A G E U N D E R S T A N D I N G FOR P E R S O N A L C O M P U T E R S George M White David Nagel Apple Computer Inc 20525 Mar[.] ... See full document

8

Classification-based spoken text selection for LVCSR language modeling

Classification-based spoken text selection for LVCSR language modeling

... a word sequence. In general, a LM is built by using a text corpus, and its performance depends on the data size and text quality. For creating LVCSR systems in dif- ferent domains and styles, it is necessary to ... See full document

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