Neural approaches to conversational AI
This tutorial surveys neural approaches to conversational AI that were developed in the last
few years. We group conversational systems into three categories:(1) question answering …
few years. We group conversational systems into three categories:(1) question answering …
From Eliza to XiaoIce: challenges and opportunities with social chatbots
Conversational systems have come a long way since their inception in the 1960s. After
decades of research and development, we have seen progress from Eliza and Parry in the …
decades of research and development, we have seen progress from Eliza and Parry in the …
Slot-gated modeling for joint slot filling and intent prediction
CW Goo, G Gao, YK Hsu, CL Huo… - Proceedings of the …, 2018 - aclanthology.org
Attention-based recurrent neural network models for joint intent detection and slot filling
have achieved the state-of-the-art performance, while they have independent attention …
have achieved the state-of-the-art performance, while they have independent attention …
Deep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: Methods, implementations, issues …
State of Charge (SOC), state of health (SOH), and remaining useful life (RUL) are the crucial
indexes used in the assessment of electric vehicle (EV) battery management systems (BMS) …
indexes used in the assessment of electric vehicle (EV) battery management systems (BMS) …
A stack-propagation framework with token-level intent detection for spoken language understanding
Intent detection and slot filling are two main tasks for building a spoken language
understanding (SLU) system. The two tasks are closely tied and the slots often highly …
understanding (SLU) system. The two tasks are closely tied and the slots often highly …
Speech model pre-training for end-to-end spoken language understanding
Whereas conventional spoken language understanding (SLU) systems map speech to text,
and then text to intent, end-to-end SLU systems map speech directly to intent through a …
and then text to intent, end-to-end SLU systems map speech directly to intent through a …
Survey on evaluation methods for dialogue systems
In this paper, we survey the methods and concepts developed for the evaluation of dialogue
systems. Evaluation, in and of itself, is a crucial part during the development process. Often …
systems. Evaluation, in and of itself, is a crucial part during the development process. Often …
A survey of joint intent detection and slot filling models in natural language understanding
Intent classification, to identify the speaker's intention, and slot filling, to label each token
with a semantic type, are critical tasks in natural language understanding. Traditionally the …
with a semantic type, are critical tasks in natural language understanding. Traditionally the …
Ml-lmcl: Mutual learning and large-margin contrastive learning for improving asr robustness in spoken language understanding
Spoken language understanding (SLU) is a fundamental task in the task-oriented dialogue
systems. However, the inevitable errors from automatic speech recognition (ASR) usually …
systems. However, the inevitable errors from automatic speech recognition (ASR) usually …
[PDF][PDF] Multi-domain joint semantic frame parsing using bi-directional rnn-lstm.
Sequence-to-sequence deep learning has recently emerged as a new paradigm in
supervised learning for spoken language understanding. However, most of the previous …
supervised learning for spoken language understanding. However, most of the previous …