Abstract
Oral English dialogue is a crucial part of a dialogue system that enables a computer to “understand” the input language as a human does, so the performance of a dialogue system is closely related to the performance of oral English dialogue understanding. In task-based human-machine dialogue systems, external knowledge bases can provide the machine with valid information beyond the training data, helping the model to better perform the oral English dialogue comprehension task. In this paper, we propose a deep recurrent neural network based on feature fusion, which directly stacks multiple nodes at a single time node to deepen the complexity of nonlinear transformations. The feature fusion network structure is applied to the ATIS dataset for oral English dialogue comprehension experiments, and the experimental results demonstrate that the feature fusion RNN network can further improve the effectiveness of the RNN network and the GRU network structure unit can obtain better results among different RNN node units.
Highlights
With the rapid development of deep learning, the field of artificial intelligence has undergone a cross-generational change [1–3]
Unlike templateand rule-based dialogue systems, deep learning-based human-computer dialogue technologies, driven by large amounts of data, are able to use neural networks to derive effective features from large-scale training data to achieve a level of understanding of user conversations and learning language expressions. e rapid growth of deep learning techniques today is largely dependent on massive amounts of chart data and rising hardware computing performance [10]
We design a candidate knowledge recall rule for rouly extracting the candidate knowledge with higher relevance from the external knowledge base; secondly, we propose a candidate knowledge attention module to filter the candidate knowledge based on the implicit information of words so as to obtain a higher quality candidate knowledge vector; and we use the joint model of oral English dialogue comprehension to complete the task of simultaneously completing intention recognition and semantic slot filling [19]
Summary
With the rapid development of deep learning, the field of artificial intelligence has undergone a cross-generational change [1–3]. E core systems of these products are called task-based dialogue systems, which can help users complete specific tasks, with the main application scenario being personal assistants. E modular human-computer dialogue system is a modulation task-based human-computer dialogue system, which is divided into the following modules as shown, namely, speech recognition and speech synthesis module, oral English dialogue comprehension module, dialogue management module, and dialogue generation module. Oral English dialogue comprehension as an important module in task-based dialogue systems is a fundamental part of current human-computer interaction technology and is a challenging and innovative research task in both academia and industry. With the rapid development of deep learning, the use of deep learning techniques can effectively improve the effectiveness of spoken language comprehension tasks, promote the development of HCI research, and better meet the needs of practical application scenarios
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