Proceedings ArticleDOI
Out-of-Domain Slot Value Detection for Spoken Dialogue Systems with Context Information
Kobayashi Yuka,Yoshida Takami,Iwata Kenji,Hiroshi Fujimura,Masami Akamine +4 more
- pp 854-861
TLDR
A Recurrent Neural Network encoder-decoder model is used and a method that uses only in-domain data is proposed that is robust against over-fitting problems because it is independent of the slot values of the training data.Abstract:
This paper proposes an approach to detecting-of-domain slot values from user utterances in spoken dialogue systems based on contexts. The approach detects keywords of slot values from utterances and consults domain knowledge (i.e., an ontology) to check whether the keywords are-of-domain. This can prevent the systems from responding improperly to user requests. We use a Recurrent Neural Network (RNN) encoder-decoder model and propose a method that uses only in-domain data. The method replaces word embedding vectors of the keywords corresponding to slot values with random vectors during training of the model. This allows using context information. The model is robust against over-fitting problems because it is independent of the slot values of the training data. Experiments show that the proposed method achieves a 65% gain in F1 score relative to a baseline model and a further 13 percentage points by combining with other methods.read more
Citations
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Proceedings ArticleDOI
Flexibly-Structured Model for Task-Oriented Dialogues.
TL;DR: This architecture is scalable to real-world scenarios and is shown through an empirical evaluation to achieve state-of-the-art performance on both the Cambridge Restaurant dataset and the Stanford in-car assistant dataset.
Journal ArticleDOI
NLP-Based Query-Answering System for Information Extraction from Building Information Models
TL;DR: In this paper , the authors developed a QA system for BIM information extraction (IE) by using natural language processing (NLP) methods to build a virtual assistant for construction project team members.
Proceedings ArticleDOI
Slot Filling with Weighted Multi-Encoders for Out-of-Domain Values.
TL;DR: A new method for slot filling of out-ofdomain (OOD) slot values, which are not included in the training data, in spoken dialogue systems, using two encoders, which distinctly encode contexts and keywords, respectively.
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Interactive teaching for conversational AI
Qing Ping,Feiyang Niu,Govind Thattai,Joel Chengottusseriyil,Qiaozi Gao,Aishwarya N. Reganti,Prashanth Rajagopal,Gokhan Tur,Dilek Hakkani-Tur,Prem Natarajan +9 more
TL;DR: A new Teachable AI system that is capable of learning new language nuggets called concepts, directly from end users using live interactive teaching sessions, and it is demonstrated that this method is very promising in leading way to build more adaptive and personalized language understanding models.
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