A Joint Model of Intent Determination and Slot Filling for Spoken Language Understanding

By Xiaodong Zhang, Houfeng Wang. 2016

301 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Bing Liu
2. Yilin Shen
3. Young-Bum Kim
4. Zixuan Ke
5. Sahisnu Mazumder
6. Wanxiang Che
7. Joo-Kyung Kim
8. Hongxia Jin

Citations in the corpus

• A Progressive Model to Enable Continual Learning for Semantic Slot Filling (2019, background): “, 2019) or jointly with the other intent detection task in SLU (Guo et al., 2014; Liu and Lane, 2016; Zhang and Wang, 2016; Wang et al., 2018; Goo et al., 2018).”
• Neural Belief Tracker: Data-Driven Dialogue State Tracking (2016, neutral): “Joint SLU/DST : Research on belief tracking has found it advantageous to reason about SLU and DST jointly, taking ASR predictions as input and generating belief states as output (Henderson et al., 2014d; Sun et al., 2014; Zilka and Jurcicek, 2015).”
• An Application-Independent Approach to Building Task-Oriented Chatbots with Interactive Continual Learning (2020, background): “However, it can form the core of an application independent task-oriented dialogue system as it provides the application independent Natural Language Understanding (NLU) functions of intent identification and slot filling [23, 50, 8].”
• A Bi-Model Based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling (2018, methodology): “It is also possible to use one joint model for intent detection and slot filling (Guo et al., 2014; Liu and Lane, 2016a,b; Zhang and Wang, 2016; HakkaniTür et al., 2016).”
• Data Augmentation by Data Noising for Open-vocabulary Slots in Spoken Language Understanding (2019, methodology): “Noised context word window: Mesnil et al. (2015) and Zhang and Wang (2016) used a context word window to improve the performance of the RNNs in slot filling.”
• An CNN-LSTM Attention Approach to Understanding User Query Intent from Online Health Communities (2017, methodology): “[9] utilized the RNN to train utterance classifier model and got the superior performance.”
• A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling (2019, background): “Considering the unsatisfactory performance of pipeline approaches caused by error propagation, the tendency is to develop a joint model (Chen et al., 2016a; Zhang and Wang, 2016) for intent detection and slot filling tasks.”
• Survey on evaluation methods for dialogue systems (2019, unknown): “2014; Zhang and Wang 2016).”
• Efficient Large-Scale Domain Classification with Personalized Attention (2018, background): “…of prior work on improving NLU systems with pre-training (Kim et al., 2015b; Celiky-ilmaz et al., 2016; Kim et al., 2017e), multi-task learning (Zhang and Wang, 2016; Liu and Lane, 2016; Kim et al., 2017b), transfer learning (El-Kahky et al., 2014; Kim et al., 2015a,c; Chen et al., 2016a; Yang…”
• Intent detection using semantically enriched word embeddings (2016, methodology): “Specifically, models for intent detection and slot filling have been jointly or independently trained with GRU, LSTM [20, 21], bidirectional LSTM [22], or bidirectional LSTM with attention mechanism in [23].”
• Adversarial Adaptation of Synthetic or Stale Data (2017, background): “…enhancing model capability and features: jointly modeling intent and slot predictions (Jeong and Lee, 2008; Xu and Sarikaya, 2013; Guo et al., 2014; Zhang and Wang, 2016; Liu and Lane, 2016a,b), modeling SLU models with web search click logs (Li et al., 2009; Kim et al., 2015a) and enhancing…”
• ONENET: Joint domain, intent, slot prediction for spoken language understanding (2017, methodology): “00 5 [17] 91.”
• A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding (2019, methodology): “Zhang and Wang (2016) first proposed the joint work using RNNs for learning the correlation between intent and slots.”