Assertion: statistical machine translation | /r/HasProperty | smt
Facets:
Source sentences:
text: For example, statistical machine translation (SMT) typically outperforms example-based machine translation (EBMT), but researchers found that when evaluating English to French translation, EBMT performs better.
text: Historically, the primary machine learning technique used in the industry was Statistical Machine Translation (SMT).
text: Statistical Machine Translation (SMT)
text: Neural machine translation (NMT) is not a drastic step beyond what has been traditionally done in statistical machine translation (SMT).
text: Statistical machine translation (SMT) is a machine translation paradigm where translations are generated on the basis of statistical models whose parameters are derived from the analysis of bilingual text corpora.
text: Statistical Machine Translation (SMT) has been the dominant translation paradigm for decades.
text: Neural machine translation (NMT) reduces post-editing effort by 25%, outputs more fluent translations, and “linguistically speaking it also seems in quite a few categories that it actually outperforms statistical machine translation (SMT).
text: This method is known as the statistical machine translation (SMT), which gained better performance than RBMT and dominated the field from the 1980s to 2000s.
text: Statistical machine translation (SMT) learns how to translate by analyzing existing human translations (known as bilingual text corpora).
text: It is a general agreement that Neural Machine Translation (NMT) has surpassed Statistical Machine Translation (SMT) in terms of fluency and adequacy when humans read the texts produced by the software.
text: The core of research on statistical machine translation (SMT) since then has been a log-linear model, where we approximate the logarithm of the true with a linear combination of many features:
text: Thanks to a shift in technology, there has been a quantum leap in machine translation quality in recent times: neural machine translation (NMT) outperforms statistical machine translation (SMT) in a wide range of set-ups and language pairs.
text: Statistical Machine Translation (SMT) was an idea born in the 1990s that translation could become an application of machine learning.
text: For instance, the term neural machine translation (NMT) emphasizes the fact that deep learning-based approaches to machine translation directly learn sequence-to-sequence transformations, obviating the need for intermediate steps such as word alignment and language modeling that was used in statistical machine translation (SMT).
text: However, while the technology is promising we still have some way to go to commercial implementations that can rival Rule Based Machine Translation (RBMT) and Statistical Machine Translation (SMT) in all use cases.
text: First of all, neural machine translation (NMT) is not a drastic step beyond what we have traditionally done in statistical machine translation (SMT).
text: There are a number of approaches to this, including statistical machine translation (SMT) based on parallel translation data, and neural machine translation (NMT) that uses deep learning technology, but our main area of expertise is a rule-based (RBMT) engine.
text: Within a year neural machine translation (NMT) had replaced statistical machine translation (SMT) as the state of the art.
text: So, what is Statistical Machine Translation (SMT)?