Spoken Language Understanding
- Natural Language Understanding
- Main Goal: The main goal of Natural Language Understanding unit is to extract information from user utterance and to construct semantic frame
- Architecture
- Feature Extraction
- Syntactic Structure based Feature : Parse Tree Kernel, Parse Path, Predicate word
- Semantic Word based Feature : Unigram Feature, Bigram Feature, Word-Pair Feature
- Feature based Prediction
- The system predicts semantic frames based on pre-built model. Models can be constructed by Probabilistic Network (CRF/Maximum Entropy Model) , Structured Support Vector Machine (SSVM) or their ensembles.
- Semantic Frame
- Semantic Frame consists of user intention information: Dialog Act and Named Entities.
- Dialog Act: The action which user anticipate for system to achieve.
- Named Entities: The values which are required for Dialog Management system to deal with users' inquiries.
- Robust Speech Understanding
- Error-Corrective Language Model Adaptation is an adaptation framework with error handling method to improve accuracy of speech recognition and performance of spoken language applications. The proposed error corrective language model adaptation approach exploits domain-specific language variations and recognition environment characteristics to provide robustness and adaptability for a spoken language system.
- Adaptation framework
- Channel Modeling with Fertility : Capturing channel characteristics with word-to-word substitution model and word fertility model, MLE for substitution model and Several discounting methods (absolute, Good-Turing, Kneser-Ney)
- Exploiting Linguistic Knowledge in Language Model Adaptation using whole-sentence exponential models (including MCMC sampling, Gaussian prior smoothing)
- ECLM adaptation is MAP process:
- Semantic Frame Extractor is to analyze the output of the speech recognition component and to assign a meaning representation that can be used by the dialogue manager. In many current spoken dialogue systems, the meaning of the utterance is derived directly from the recognized string using a “semantic frame”.
- Procedure
- Input : Utterances
- Output: Meaningful concepts
- Linguistic feature generator selects useful syntactic, semantic, cognitive features based on POS tagging, chunking, and parsing.
- Machine learning techniques train models to represent linguistic character based Maximum Entropy models, Conditional Random Fields, and Neural Networks.
- Main goal
- Clearing ambiguity in natural language (Lexical/Sense/Structural ambiguity)
- Robust handling for ill-formed spoken input
- Unsupervised Spoken Language Understanding
- Dialog Corpus
- Essential resource for the data-driven spoken dialog system
- Should be labeled with a semantic annotation
- Human annotation process
- Require tedious human efforts
- Goal
- To reduce human efforts
- We address the SLU problem in an unsupervised manner
- Overall procedure
- Based on HDP-HMM
- Contact
One of the main issues on natural language processing is to understand intention from user utterance. We have been developing Spoken Language Understanding (SLU) techniques for speech dialogue system which assures sufficient robustness and flexibility. We present a new language model adaptation method to recover in-complete inputs from speech. Several machine learning methods have been applied for our supervised/unsupervised SLU systems to extract semantic concept from users’ utterances. We also have developed several techniques consists of linguistic feature process, information extraction, relational data learning to construct SLU techniques.
