INDONESIAN TASK-ORIENTED DIALOGUE SYSTEM USING END-TO-END APPROACH
A Task-oriented dialogue system (ToDS) is a conversational agent designed to communicate with users in natural language, assisting them in completing various user tasks i.e. making restaurant reservations or purchasing tickets. Recently, many researchers are interested in using the end-to-end app...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/73923 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | A Task-oriented dialogue system (ToDS) is a conversational agent designed to communicate
with users in natural language, assisting them in completing various user tasks i.e. making
restaurant reservations or purchasing tickets. Recently, many researchers are interested in
using the end-to-end approach to build the ToDS framework, as it offers simplicity compared
to the other approach. However, since there is no ToDS corpus available in Indonesia, which
is an under-represented language, an Indonesian ToDS benchmark has never been conducted.
The main focus of this thesis involves the construction of two ToDS training corpora
specifically designed for the Indonesian language. Additionally, the thesis includes
conducting experiments using two distinct ToDS frameworks, while utilizing the manually
created corpora. The total number of constructed dialogues for the Indonesian ToDS corpus
in this thesis amounts to 999 dialogues, originating from two English ToDS corpora named
CamRest and SMD. The experiments conducted in this study evaluate the frameworks in
three types of experiments: monolingual, bilingual, and cross-lingual transfer learning. The
end-to-end frameworks tested in this thesis are Sequicity and MinTL, which differ in the type
of word embedding employed by each framework.
The experiment results reveal that the language adaptation process incorporating two sets of
Indonesian and English corpora during the training phase of the ToDS framework has a
positive impact on the model's capability to successfully complete user-given tasks.
Furthermore, bilingual experiments consistently yield the best metric values across all
experiments. However, both frameworks struggle to achieve satisfactory results in
cross-lingual experiments due to the language disparity between the training and testing
corpora. |
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