[PDF] Top 20 Re Ranking Models for Spoken Language Understanding
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Re Ranking Models for Spoken Language Understanding
... probability of concepts and constituents; and (ii) discriminative models, which learn a classifica- tion function to map words into concepts based on geometric and statistical properties. An ex- ample of ... See full document
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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
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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. Models ... See full document
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Exploiting multiple hypotheses for Multilingual Spoken Language Understanding
... fort in the adaptation of the models. It has been shown that the use of graphs of words, as a mech- anism of generalization and transmission of hy- potheses, is a good approach to recover from er- rors generated ... See full document
9
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
ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re Ranking
... Question Answering in natural language often re- quires deeper linguistic understanding and reason- ing over multiple sentences. For complex ques- tions, it is very unlikely to build or have a knowl- ... See full document
7
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 ... See full document
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Neural Lexicons for Slot Tagging in Spoken Language Understanding
... involving spoken language understanding for the slot tag- ging ...other models. Mesnil et al. (2015) evaluate several RNN-based models for slot tagging and show the RNN-based ... See full document
7
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
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Sequential Dialogue Context Modeling for Spoken Language Understanding
... Spoken Language Understanding (SLU) is a key component of goal oriented di- alogue systems that would parse user ut- terances into semantic frame representa- ...based language ... See full document
12
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
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Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding
... on Spoken Language Understanding (SLU), semantic decoding is usually seen as a sequence tagging problem with models trained and tested on datasets with word-level annotations (T¨ur et ... See full document
10
Nonparametric Bayesian Models for Spoken Language Understanding
... Since we use the CRF as a candidate generator, we expect that the CRF N-best can rank the correct answer higher in the candidate list. In fact, the top five candidates cover almost all of the correct an- swers. ... See full document
9
Combining Statistical and Knowledge Based Spoken Language Understanding in Conditional Models
... This paper has introduced a conditional model framework that integrates statistical learning with a knowledge-based approach to SLU. We have shown that a conditional model reduces SLU slot error rate by more than 20% ... See full document
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An Arabic Multi Domain Spoken Language Understanding System
... their re- quests to access their information from the edu- cation ...repeated re- quests, we obtained a corpus made of 127 differ- ent requests expressed in ...students re- quest from the office of ... See full document
5
Extracting Clauses for Spoken Language Understanding in Conversational Systems
... Language Processing EMNLP, Philadelphia, July 2002, pp.. Proceedings of the Conference on Empirical Methods in Natural.[r] ... See full document
8
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
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Selecting Machine Translated Data for Quick Bootstrapping of a Natural Language Understanding System
... model language use by Ger- man customers well and can hence potentially de- grade performance of statistical models trained on these ...source language and target language use, we used ... See full document
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