A joint model based on interactive gate mechanism for spoken language understanding

2021 ◽  
Author(s):  
Chengai Sun ◽  
Liangyu Lv ◽  
Tailu Liu ◽  
Tangjun Li
Keyword(s):  
Spoken Language ◽  
Joint Model ◽  
Language Understanding ◽  
Spoken Language Understanding ◽  
Model Based

scholarly journals Joint Model-Based Attention for Spoken Language Understanding Task

2020 ◽  
Vol 12 (4) ◽  
pp. 32-43
Author(s):  
Xin Liu ◽  
RuiHua Qi ◽  
Lin Shao
Keyword(s):  
Contextual Information ◽  
Spoken Language ◽  
Joint Model ◽  
Shared Task ◽  
Global Features ◽  
Language Understanding ◽  
Spoken Language Understanding ◽  
Model Based ◽  
Sentence Level ◽  
Slot Filling

Intent determination (ID) and slot filling (SF) are two critical steps in the spoken language understanding (SLU) task. Conventionally, most previous work has been done for each subtask respectively. To exploit the dependencies between intent label and slot sequence, as well as deal with both tasks simultaneously, this paper proposes a joint model (ABLCJ), which is trained by a united loss function. In order to utilize both past and future input features efficiently, a joint model based Bi-LSTM with contextual information is employed to learn the representation of each step, which are shared by two tasks and the model. This paper also uses sentence-level tag information learned from a CRF layer to predict the tag of each slot. Meanwhile, a submodule-based attention is employed to capture global features of a sentence for intent classification. The experimental results demonstrate that ABLCJ achieves competitive performance in the Shared Task 4 of NLPCC 2018.

scholarly journals A Survey on Spoken Language Understanding: Recent Advances and New Frontiers

2021 ◽  
Author(s):  
Libo Qin ◽  
Tianbao Xie ◽  
Wanxiang Che ◽  
Ting Liu
Keyword(s):  
Joint Modeling ◽  
Spoken Language ◽  
Joint Model ◽  
Research Field ◽  
Language Models ◽  
Future Research ◽  
Language Understanding ◽  
Spoken Language Understanding ◽  
Recent Advances ◽  
Dialog System

Spoken Language Understanding (SLU) aims to extract the semantics frame of user queries, which is a core component in a task-oriented dialog system. With the burst of deep neural networks and the evolution of pre-trained language models, the research of SLU has obtained significant breakthroughs. However, there remains a lack of a comprehensive survey summarizing existing approaches and recent trends, which motivated the work presented in this article. In this paper, we survey recent advances and new frontiers in SLU. Specifically, we give a thorough review of this research field, covering different aspects including (1) new taxonomy: we provide a new perspective for SLU filed, including single model vs. joint model, implicit joint modeling vs. explicit joint modeling in joint model, non pre-trained paradigm vs. pretrained paradigm; (2) new frontiers: some emerging areas in complex SLU as well as the corresponding challenges; (3) abundant open-source resources: to help the community, we have collected, organized the related papers, baseline projects and leaderboard on a public website where SLU researchers could directly access to the recent progress. We hope that this survey can shed a light on future research in SLU field.

scholarly journals CDS: Contextual Spoken Language Understanding Model Based on Dual Semantics

2020 ◽  
Vol 1651 ◽  
pp. 012051
Author(s):  
Kun Ma ◽  
Minghao Hu ◽  
Fangzhao Li ◽  
Li Luo
Keyword(s):  
Spoken Language ◽  
Language Understanding ◽  
Spoken Language Understanding ◽  
Model Based

Integrating Syntax and Semantics into Spoken Language Understanding

1991 ◽  
Author(s):  
Lynette Hirschman ◽  
Stephanie Seneff ◽  
David Goodine ◽  
Michael Phillips
Keyword(s):  
Spoken Language ◽  
Language Understanding ◽  
Spoken Language Understanding

Spoken Language Understanding using Pretraining Technique

2020 ◽  
Author(s):  
Saad Ghojaria ◽  
Rahul Kotian ◽  
Yash Sawant ◽  
Suresh Mestry
Keyword(s):  
Spoken Language ◽  
Language Understanding ◽  
Spoken Language Understanding

End-to-End Memory Networks with Knowledge Carryover for Multi-Turn Spoken Language Understanding

2016 ◽  
Author(s):  
Yun-Nung Chen ◽  
Dilek Hakkani-Tür ◽  
Gokhan Tur ◽  
Jianfeng Gao ◽  
Li Deng
Keyword(s):  
Spoken Language ◽  
Language Understanding ◽  
Spoken Language Understanding ◽  
End To End

scholarly journals RNN Based Incremental Online Spoken Language Understanding

2021 ◽  
Author(s):  
Prashanth Gurunath Shivakumar ◽  
Naveen Kumar ◽  
Panayiotis Georgiou ◽  
Shrikanth Narayanan
Keyword(s):  
Spoken Language ◽  
Language Understanding ◽  
Spoken Language Understanding
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