Year: 2024
Type: dissertation
Abstract: These days it is becoming common to communicate with international people as globalization expands.Although many international conferences are held, not all the speakers and listeners are good at English.Therefore, interpreters sometimes attend such conferences.For the interpretation in conferences and negotiations, we often require simultaneous interpretation which starts to translate before the speaker finishes the utterance.Simultaneous interpretation is important because the meeting time becomes much longer if we wait for the entire utterance before translation.Also, we sometimes tell jokes in negotiation, and it is funnier if the translation of a joke is done immediately without any interval.We would like to hire simultaneous interpreters for every international event, but it is highly costly.Therefore, there is a huge demand for simultaneous machine translation which can be used at a lower cost.Recent studies on machine translation are based on neural networks and they produce more fluent output than traditional statistical machine translation.Although machine translation research has evolved rapidly, simultaneous translation is still a difficult task.This is because the machine needs to output partial translation without reading the latter part of the input sentence as context.A lot of previous research on simultaneous translation uses the translation model pre-trained with bilingual full-sentence pairs.However, at the inference step, the model needs to translate only a partial input much shorter than a full sentence.Therefore, there is a gap between the training of translation models and
DOI: 10.34413/dr.02042
Author: 加納, 保昌
Institution: Nara Institute of Science and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7144228045