Conversational AI
An Explication of Few-Shot Learning Problem in Transformers-Based Chatbot Systems
Bibliographic Data
| ID | 22107465 |
|---|---|
| Authors | Muzamil Ahmed (0000-0001-6940-7835, COMSATS University Islamabad), Hikmat Ullah Khan (0000-0002-8178-6652, COMSATS University Islamabad), Ehsan Ullah Munir (0000-0001-7838-0291, COMSATS University Islamabad) |
| Year | 2024 |
| Volume | 11 |
| Issue | 2 |
| Pages | 1888-1906 |
| Publication date | 2024-04-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2023.3281492 |
| OpenAlex | W4380785202 |
| Language | EN |
| Citations received | 2 |
| References cited | 43 |
With the recent advancements in conversational artificial intelligence (AI), the practical applications of chatbots have risen significantly in diverse domains such as healthcare, education, e-government, customer support systems, social platforms, and entertainment. The chatbot converses with humans in natural language and responds to their queries with precise and relevant answers. The relevant literature presents several methods for intent classification and slot mapping for chatbot question answering. However, the existing chatbot architectures still suffer from few-shot learning problem (imbalance ratio of class labels over samples) and ineffective dialog management to retain the context and slots mapping. This study aims to introduce the architecture of chatbot by focusing few-shot learning problem and context management in dialog-based conversations. First, to mitigate the few-shot learning problem with chatbots, we propose a novel hybrid intent and slots transformers (HIST) model. The HIST chatbot architecture utilizes transformers and self-attention mechanisms along with bigated recurrent unit and combines conditional random field algorithms for intent classification and slot extraction. Second, to address dialog management, we introduce a hybrid interaction strategy for slots mapping and effective conversational context management. To validate the proposed model’s effectiveness, comprehensive empirical analysis is carried out using three benchmark datasets including airline travel information system (ATIS), banking77, and conversational language interface for natural conversation 150 (CLINC150). The results show that HIST outperforms against the state-of-the-art existing methods with a clear margin and obtained an accuracy of 94.89% and 96.17% for intent classification and slots extraction, respectively. The empirical results confirm the effectiveness of the HIST chatbot for resolving the few-shot learning problem with effective dialog management in chatbot systems
Chatbot · Electrical engineering · Explication · Human–computer interaction · Linguistics · Natural language processing · One shot · Speech recognition · Transformer · AI in Service Interactions · Computer Science · Engineering · Multi-Agent Systems and Negotiation · Topic Modeling · Artificial Intelligence
| Citations per year | 2 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |