A Multi-Attention Approach Using BERT and Stacked Bidirectional LSTM for Improved Dialogue State Tracking

• Published in: Applied sciences Vol. 13; no. 3; p. 1775
• Main Authors: Khan, Muhammad Asif, Huang, Yi, Feng, Junlan, Prasad, Bhuyan Kaibalya, Ali, Zafar, Ullah, Irfan, Kefalas, Pavlos
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-02-2023
• Subjects: attention mechanism; BERT; Classification; classification problem; Datasets; dialogue state tracking; Interactive computer systems; Language; Natural language; Neural networks; Ontology; Semantics; Speech; spoken dialogue systems; stacked BiLSTM; Verbal communication
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13031775

The modern digital world and associated innovative and state-of-the-art applications that characterize its presence, render the current digital age a captivating era for many worldwide. These innovations include dialogue systems, such as Apple’s Siri, Google Now, and Microsoft’s Cortana, that stay on the personal devices of users and assist them in their daily activities. These systems track the intentions of users by analyzing their speech, context by looking at their previous turns, and several other external details, and respond or act in the form of speech output. For these systems to work efficiently, a dialogue state tracking (DST) module is required to infer the current state of the dialogue in a conversation by processing previous states up to the current state. However, developing a DST module that tracks and exploit dialogue states effectively and accurately is challenging. The notable challenges that warrant immediate attention include scalability, handling the unseen slot-value pairs during training, and retraining the model with changes in the domain ontology. In this article, we present a new end-to-end framework by combining BERT, Stacked Bidirectional LSTM (BiLSTM), and a multiple attention mechanism to formalize DST as a classification problem and address the aforementioned issues. The BERT-based module encodes the user’s and system’s utterances. The Stacked BiLSTM extracts the contextual features and multiple attention mechanisms to calculate the attention between its hidden states and the utterance embeddings. We experimentally evaluated our method against the current approaches over a variety of datasets. The results indicate a significant overall improvement. The proposed model is scalable in terms of sharing the parameters and it considers the unseen instances during training.