A COMPARATIVE STUDY ON LANGUAGE MODELS FOR TASK-ORIENTED DIALOGUE SYSTEMS
The recent development of language models has shown promising results by achieving state-of-the-art performance on various natural language tasks by finetuning pre-trained models. In task-oriented dialogue (ToD) systems, language models can be used for end-to-end training without relying on dialog...
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| Main Author: | |
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/56938 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | The recent development of language models has shown promising results by
achieving state-of-the-art performance on various natural language tasks by finetuning pre-trained models. In task-oriented dialogue (ToD) systems, language
models can be used for end-to-end training without relying on dialogue state tracking
to track the dialogue history but allowing the language models to generate responses
according to the context given as input. This paper conducts a comparative study
to show the effectiveness and strength of using recent pre-trained models for finetuning, such as BART and T5, on end-to-end ToD systems. The experimental results
show substantial performance improvements after language model fine-tuning. The
models produce more fluent responses after adding knowledge to the context that
guides the model to avoid hallucination and generate accurate entities in the generated
responses. Furthermore, we found that BART and T5 outperform GPT-based models
in BLEU and F1 scores and achieve state-of-the-art performance in a ToD system. |
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