2000 character limit reached
Phrase Based Language Model for Statistical Machine Translation: Empirical Study (1501.05203v3)
Published 21 Jan 2015 in cs.CL
Abstract: Reordering is a challenge to machine translation (MT) systems. In MT, the widely used approach is to apply word based LLM (LM) which considers the constituent units of a sentence as words. In speech recognition (SR), some phrase based LM have been proposed. However, those LMs are not necessarily suitable or optimal for reordering. We propose two phrase based LMs which considers the constituent units of a sentence as phrases. Experiments show that our phrase based LMs outperform the word based LM with the respect of perplexity and n-best list re-ranking.
Collections
Sign up for free to add this paper to one or more collections.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
