Machine translation is an essential technology in the modern world, enabling people from different cultures to communicate efficiently. It has many subtypes, one of which is statistical machine translation. Although not as efficient as modern neural network-based solutions, statistical machine translation is still widely used in many professional domains.
In this topic, you will learn what statistical machine translation is, how it emerged, its underlying principles, its main types, and its current challenges, such as statistical anomalies and alignment issues.
What is statistical machine translation?
Statistical Machine Translation (SMT) is an approach to machine translation that gained prominence after the rule-based methods. Unlike its predecessor, which requires an exhaustive and precise description of all linguistic rules for high-quality translation, SMT offers more versatility. Although it has been somewhat overshadowed by neural network-based machine translation, SMT remained popular throughout the 2010s. For example, Google Translate utilized this technology until 2016.
Statistical machine translation engines lack natural language understanding but can learn linguistic patterns by analyzing large sets of sentences in one language and their corresponding translations in another. This allows them to deduce which words and phrases frequently appear together. When given a sentence in one language, SMT uses these patterns to estimate a translation into another language. While the method is not flawless and may produce errors, it generally provides stable and accurate translations, especially if the original text does not contain domain-specific language.
Basis of statistical machine translation
The concept underlying statistical machine translation involves using probabilities to translate one language into another. For example, let's say we have a document in German (the source language) that we want to translate into English (the target language). The goal is to find the most accurate translation. To achieve this, we employ Bayes' theorem, which helps us calculate the probability that a specific English sentence is the correct translation for a given German sentence. This calculation relies on two key components:
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Translation Model: This model gauges how likely it is that the German sentence will translate into a specific English sentence. It aims to preserve the original sentence's meaning, thereby ensuring its adequacy.
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Language Model: This model is trained using data from the target language (English, in this case). It evaluates how likely a given English sentence would appear in a typical English text, essentially selecting sentences that are more likely to make sense and ensuring fluency in the resulting translation.
First, the source document is divided into units, which are then matched with their corresponding translations from the translation model. Next, the language model verifies that the translation for each unit is likely to be accurate in the target language. Finally, the engine then returns the English sentence most likely to be both correct and idiomatic.
Types of statistical machine translation
Statistical machine translation comes in several subtypes, each taking into account factors such as the units of text to be translated and the syntactic and hierarchical relationships within the text.
Phrase-based approach: This method aims to improve the translation of text sequences by translating groups of words collectively rather than individually. These groups, known as "phrases," are matched with corresponding phrases found in large corpora using the statistical methods described earlier. While effective, this approach has proven to be less accurate than other subtypes.
Syntax-based approach: This approach focuses on translating entire syntactic structures, accounting for the organization of words within sentences. This method gained popularity with the advent of advanced computer programs capable of processing syntax. Rather than translating word-by-word, this approach considers the relationships between parts of a sentence, resulting in more accurate and coherent translations.
Hierarchical phrase-based approach: This is a hybrid method that combines elements of both the phrase-based and syntax-based translation approaches. It employs rules to determine how phrases should fit together, allowing for more accurate translations even when these rules diverge from traditional grammar. This approach strikes a balance between the flexibility of phrase-based translation and the structural rigor of syntax-based translation.
Common challenges of statistical machine translation
Statistical machine translation faces several limitations, including alignment problems, out-of-vocabulary words, and issues arising from syntactic complexity.
Alignment issues: One such limitation is syntactic alignment, which can be described as follows: Parallel texts in two languages may have sentences that don't align perfectly. For example, a long sentence in one language might translate into several shorter sentences in another, particularly in languages without clear sentence-ending indicators.
Word alignment: This is another type of alignment issue, concerning the correct matching of words between two languages. This becomes especially tricky when there are no direct equivalents in the target language for function words in the source language. Different alignment algorithms and models can mitigate this issue, but they do not eliminate it entirely.
Statistical anomalies: These occur when the initial training data is unbalanced, skewing the translation in a particular direction. For instance, the sentence "We moved to New York last fall" might be translated as "We moved to Berlin last fall" if the training data had shown the latter phrase more frequently.
Idiomatic phrases: Translation engines often struggle with idioms. For example, the phrase "take something at face value" could be translated word-for-word, losing its metaphorical meaning. Idioms generally need to be matched as whole phrases to retain their intended meaning in the target language.
Word order: The different syntax of source and target languages can also pose challenges. For instance, while one might say, "I want to do homework" in one language, the word order in another could be "I homework to do want." Properly restructuring the sentence in the target language can be complex.
Out-of-vocabulary words: These present a challenge because if a word has not been part of the training data, the engine will not be able to translate it. This issue can be partially mitigated by providing more diverse training data, which could include a wider range of word forms and less conventional contexts.
Conclusion
Statistical machine translation represents a pivotal stage in the evolution of machine translation technologies. Utilizing probability-based models, it aims to translate text between languages both adequately and accurately. The method has three main subtypes, each offering varying levels of accuracy: phrase-based, syntax-based, and hierarchical phrase-based approaches. While statistical machine translation performs relatively well on generic texts that do not require domain-specific knowledge, it faces challenges. These include issues with word and sentence-level alignment, the translation of idioms, and handling of rare words and expressions.