This paper studies three techniques that improve the quality of N-best hypotheses through additional regeneration process. Unlike the multi-system consensus ...
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Nov 9, 2024 · This paper studies three techniques that improve the quality of N-best hypotheses through additional regeneration process.
We implement three methods to regenerate new hypotheses: re-decoding, n-gram expansion and confusion network. Re-decoding (Rosti et al.,. 2007a) based ...
[PDF] Regenerating Hypotheses for Statistical Machine Translation ...
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Three techniques that improve the quality of N-best hypotheses through additional regeneration process: redecoding, n-gram expansion, and confusion ...
This paper studies three techniques that improve the quality of N-best hypotheses through additional regeneration process. Unlike the multi-system consensus ...
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Regenerating Hypotheses for Statistical Machine Translation. record by Min Zhang • Regenerating Hypotheses for Statistical Machine Translation. Boxing Chen ...
In this paper, we present a novel approach to improve the final translation result by dynamically augmenting the translation scores of hypotheses that derived ...
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<title>Regenerating Hypotheses for Statistical Machine Translation</title>. <introduction>State-of-the-art Statistical Machine Translation (SMT) systems ...
It is also known as \"automatic speech recognition\" (ASR), \"computer speech recognition\", or just \"speech to text\" (STT). Some SR systems use \"training\" ...
2007. This paper presents a simple and robust consensus decoding approach for combining multiple machine translation (MT) system outputs.