AI for Online Customer Service: Intent Recognition and Slot Filling Based on Deep Learning Technology

Wu, Yirui; Mao, Wenqin; Feng, Jun

doi:10.1007/s11036-021-01795-5

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…With powerful modeling ability in deep learning, the end-to-end speech synthesis system does not need any text processing step to synthesize clear speech while the data is enough. Recently, researchers have proposed the method of applying cloud computing to the end-to-end model, which reduces the gap between end-to-end model and practical application [9]. But in most languages, data is always lacking, so text processing is necessary.…”

Section: Related Workmentioning

confidence: 99%

A New Speech Encoder Based on Dynamic Framing Approach

Liu¹,

Yang²,

Zhou³

2023

Computer Modeling in Engineering &Amp; Sciences

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Latent information is difficult to get from the text in speech synthesis. Studies show that features from speech can get more information to help text encoding. In the field of speech encoding, a lot of work has been conducted on two aspects. The first aspect is to encode speech frame by frame. The second aspect is to encode the whole speech to a vector. But the scale in these aspects is fixed. So, encoding speech with an adjustable scale for more latent information is worthy of investigation. But current alignment approaches only support frame-by-frame encoding and speech-to-vector encoding. It remains a challenge to propose a new alignment approach to support adjustable scale speech encoding. This paper presents the dynamic speech encoder with a new alignment approach in conjunction with frame-by-frame encoding and speech-to-vector encoding. The speech feature from our model achieves three functions. First, the speech feature can reconstruct the origin speech while the length of the speech feature is equal to the text length. Second, our model can get text embedding from speech, and the encoded speech feature is similar to the text embedding result. Finally, it can transfer the style of synthesis speech and make it more similar to the given reference speech.

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Section: Related Workmentioning

confidence: 99%

A New Speech Encoder Based on Dynamic Framing Approach

Liu¹,

Yang²,

Zhou³

2023

Computer Modeling in Engineering &Amp; Sciences

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“…proposed a multi-tasking technique which integrates intent detection, slot-filling and dialogue classification to outperform individual and pipeline models. The attention layer and the stacked Bi-LSTM layer and Bi-GRU is used in the work for effective results Wu et al (2021). proposed a Bi-GRU is used for extracting features and CNN based model is used for intent classification and CRF model is used to fill slots.…”

mentioning

confidence: 99%

The Context-based approach of Slot Label Creation and Slot Filling for Interactive Systems

Magoo

Singh

2023

Preprint

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Slot-filling is considered one of the most important components of task-based interactive systems. The main challenge in slot filling is to assign a semantic label to each text in the utterance which is further useful for generating the response by the interactive system. Another challenge is the non-availability of extensive labeled data. Thus in this research, we primarily focus on developing two approaches namely (i) automatic slot identification through IOB labeling for slot labels creation and preparing a labeled dataset (ii) a self-learning based model where the slot labels are created in step (i) are predicted for an untrained query from the same or different domains. We use BI-LSTM with Elmo Embedding for our proposed frameworks with POS Tags to generate slot-independent tags and label descriptions for slot labeling. We applied and compared our model on the SNIPS and Mobile service provider dataset through the F1 score and analyzed that the efficiency of self-learning-based models highly depends upon the quality and correlation among different domains of the same dataset.

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Business chatbots with deep learning technologies: state-of-the-art, taxonomies, and future research directions

Zhang,

Lau,

David Xu

et al. 2024

Artif Intell Rev

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With the support of advanced hardware and software technology, Artificial Intelligence (AI) techniques, especially the increasing number of deep learning algorithms, have spawned the popularization of online intelligent services and accelerated the contemporary development and applications of chatbot systems. The promise of providing 24/7 uninterrupted business services and minimizing workforce costs has made business chatbots a hot topic due to the impact of the pandemic. It has attracted considerable attention from academic researchers and business practitioners. However, a thorough technical review of advanced chatbot technologies and their relevance and applications to various business domains is rare in the literature. The main contribution of this review article is the critical analysis of various chatbot development approaches and the underlying deep learning computational methods in the context of some business applications. We first conceptualize current business chatbot architectures and illustrate the technical characteristics of two common structures. Next, we explore the mainstream deep learning technologies in chatbot design from the perspective of computational methods and usages. Then, we propose a new framework to classify chatbot construction architectures and differentiate the traditional retrieval-based and generation-based chatbots in terms of the modern pipeline and end-to-end structures. Finally, we highlight future research directions for business chatbots to enable researchers to devote their efforts to the most promising research topics and commercial scenarios and for practitioners to benefit from realizing the trend in business chatbot development and applications.

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AI for Online Customer Service: Intent Recognition and Slot Filling Based on Deep Learning Technology

Cited by 6 publications

References 32 publications

A New Speech Encoder Based on Dynamic Framing Approach

A New Speech Encoder Based on Dynamic Framing Approach

The Context-based approach of Slot Label Creation and Slot Filling for Interactive Systems

Business chatbots with deep learning technologies: state-of-the-art, taxonomies, and future research directions

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