Lost in Interpretation: Predicting Untranslated Terminology in Simultaneous Interpretation (N19-1)
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| Challenge: | Experimental results on a newly-annotated version of the NAIST Simultaneous Translation Corpus indicate the promise of our proposed method. |
| Approach: | They propose a task of predicting which terminology simultaneous interpreters will leave untranslated using supervised sequence taggers. |
| Outcome: | The proposed method predicts which terminology interpreters leave untranslated . it is based on an annotated version of the NAIST Simultaneous Translation Corpus . |
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| Challenge: | a comprehensive survey of cutting-edge weakly-supervised and unsupervised cross-lingual word representations is presented . |
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Barriers to Effective Evaluation of Simultaneous Interpretation (2024.findings-eacl)
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| Challenge: | Existing studies have relied on out-of-the-box machine translation metrics to evaluate interpretation data, but they do not account for human judgments of interpretation quality. |
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Simultaneous Translation (2020.emnlp-tutorials)
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| Challenge: | Simultaneous translation is a problem that has long been considered one of the hardest problems in AI . this tutorial will provide a deep understanding of the history and the recent advances in simultaneous translation. |
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Speech Translation and the End-to-End Promise: Taking Stock of Where We Are (2020.acl-main)
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| Challenge: | Until recently, the only feasible approach to translating acoustic speech signals into text was the cascaded approach. |
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| Challenge: | Unsupervised neural machine translation (UNMT) has achieved impressive results, but there are still several challenges for the technology. |
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Simultaneous Interpretation Corpus Construction by Large Language Models in Distant Language Pair (2024.emnlp-main)
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| Challenge: | Existing siMT corpora are limited due to high costs and limited annotator capabilities. |
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A General Framework for Adaptation of Neural Machine Translation to Simultaneous Translation (2020.aacl-main)
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| Challenge: | Despite the success of neural machine translation, simultaneous neural machine translators are challenging due to syntactic structure difference and simultaneity requirements. |
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Statistical Analysis of Missing Translation in Simultaneous Interpretation Using A Large-scale Bilingual Speech Corpus (L18-1)
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| Challenge: | Various types of omissions have been described in simultaneous interpretation to improve interpretation quality or train interpreters. |
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