Misplaced Pages

#332667

58-817: Google Neural Machine Translation (GNMT) was a neural machine translation (NMT) system developed by Google and introduced in November 2016 that used an artificial neural network to increase fluency and accuracy in Google Translate . The neural network consisted of two main blocks, an encoder and a decoder, both of LSTM architecture with 8 1024-wide layers each and a simple 1-layer 1024-wide feedforward attention mechanism connecting them. The total number of parameters has been variously described as over 160 million, approximately 210 million, 278 million or 380 million. It used WordPiece tokenizer , and beam search decoding strategy. It ran on Tensor Processing Units . By 2020,

116-523: A r g m a x θ ∑ i T ∏ j = 1 J ( i ) P ( y j ( i ) | y 1 , j − 1 ( i ) , x ( i ) ) {\displaystyle \theta ^{*}={\underset {\theta }{\operatorname {arg\,max} }}\sum _{i}^{T}\prod _{j=1}^{J^{(i)}}P(y_{j}^{(i)}|y_{1,j-1}^{(i)},\mathbf {x} ^{(i)})} Since we are only interested in

174-468: A convolutional neural network (CNN) for encoding the source and both Cho et al. and Sutskever et al. using a recurrent neural network (RNN) instead. All three used an RNN conditioned on a fixed encoding of the source as their decoder to produce the translation. However, these models performed poorly on longer sentences. This problem was addressed when Bahdanau et al. introduced attention to their encoder-decoder architecture: At each decoding step,

232-727: A Real Character and a Philosophical Language". In the 18th and 19th centuries many proposals for "universal" international languages were developed, the most well known being Esperanto . That said, applying the idea of a universal language to machine translation did not appear in any of the first significant approaches. Instead, work started on pairs of languages. However, during the 1950s and 60s, researchers in Cambridge headed by Margaret Masterman , in Leningrad headed by Nikolai Andreev and in Milan by Silvio Ceccato started work in this area. The idea

290-565: A number of ways: A generative LLM can be prompted in a zero-shot fashion by just asking it to translate a text into another language without giving any further examples in the prompt. Or one can include one or several example translations in the prompt before asking to translate the text in question. This is then called one-shot or few-shot learning , respectively. For example, the following prompts were used by Hendy et al. (2023) for zero-shot and one-shot translation: Interlingual machine translation Interlingual machine translation

348-412: A particular game ] vectors, so they can be processed mathematically. NMT models assign a probability P ( y | x ) {\displaystyle P(y|x)} to potential translations y and then search a subset of potential translations for the one with the highest probability. Most NMT models are auto-regressive : They model the probability of each target token as a function of

406-405: A process called self-attention . Since the attention mechanism does not have any notion of token order, but the order of words in a sentence is obviously relevant, the token embeddings are combined with an explicit encoding of their position in the sentence . Since both the transformer's encoder and decoder are free from recurrent elements, they can both be parallelized during training. However,

464-475: A starting point for learning other tasks has proven very successful in wider NLP , this paradigm is also becoming more prevalent in NMT. This is especially useful for low-resource languages, where large parallel datasets do not exist. An example of this is the mBART model, which first trains one transformer on a multilingual dataset to recover masked tokens in sentences, and then fine-tunes the resulting autoencoder on

522-655: A time due to its recurrent nature. In the same year, “Microsoft Translator released AI-powered online neural machine translation (NMT). DeepL Translator , which was at the time based on a CNN encoder , was also released in the same year and was judged by several news outlets to outperform its competitors. It has also been seen that OpenAI 's GPT-3 released in 2020 can function as a neural machine translation system. Some other machine translation systems, such as Microsoft translator and SYSTRAN can be also seen to have integrated neural networks into their operations. Another network architecture that lends itself to parallelization

580-440: A translation pair between each pair of languages in the system. So instead of creating n ( n − 1 ) {\displaystyle n(n-1)} language pairs, where n {\displaystyle n} is the number of languages in the system, it is only necessary to make 2 n {\displaystyle 2n} pairs between the n {\displaystyle n} languages and

638-465: Is available, and with domain shift between the data a system was trained on and the texts it is supposed to translate. NMT systems also tend to produce fairly literal translations. In the translation task, a sentence x = x 1 , I {\displaystyle \mathbf {x} =x_{1,I}} (consisting of I {\displaystyle I} tokens x i {\displaystyle x_{i}} ) in

SECTION 10

#1732855715333

696-416: Is closer, or more aligned with the target language, and this could improve the translation quality. The above-mentioned system is based on the idea of using linguistic proximity to improve the translation quality from a text in one original language to many other structurally similar languages from only one original analysis. This principle is also used in pivot machine translation , where a natural language

754-525: Is done iteratively on small subsets (mini-batches) of the training set using stochastic gradient descent . During inference, auto-regressive decoders use the token generated in the previous step as the input token. However, the vocabulary of target tokens is usually very large. So, at the beginning of the training phase, untrained models will pick the wrong token almost always; and subsequent steps would then have to work with wrong input tokens, which would slow down training considerably. Instead, teacher forcing

812-456: Is it directly translates one language into another. For example, it might be trained just for Japanese-English and Korean-English translation, but can perform Japanese-Korean translation. The system appears to have learned to produce a language-independent intermediate representation of language (an " interlingua "), which allows it to perform zero-shot translation by converting from and to the interlingua. Google Translate previously first translated

870-443: Is it directly translates one language into another. For example, it might be trained just for Japanese-English and Korean-English translation, but can perform Japanese-Korean translation. The system appears to have learned to produce a language-independent intermediate representation of language (an "interlingua"), which allows it to perform zero-shot translation by converting from and to the interlingua. In this method of translation,

928-405: Is one of the classic approaches to machine translation . In this approach, the source language, i.e. the text to be translated is transformed into an interlingua, i.e., an abstract language-independent representation. The target language is then generated from the interlingua. Within the rule-based machine translation paradigm, the interlingual approach is an alternative to the direct approach and

986-407: Is possible that one of the two covers more of the characteristics of the source language, and the other possess more of the characteristics of the target language. The translation then proceeds by converting sentences from the first language into sentences closer to the target language through two stages. The system may also be set up such that the second interlingua uses a more specific vocabulary that

1044-506: Is that the definition of an interlingua is difficult and maybe even impossible for a wider domain. The ideal context for interlingual machine translation is thus multilingual machine translation in a very specific domain. For example, Interlingua has been used as a pivot language in international conferences and has been proposed as a pivot language for the European Union . The first ideas about interlingual machine translation appeared in

1102-399: Is the transformer , which was introduced by Vaswani et al. also in 2017. Like previous models, the transformer still uses the attention mechanism for weighting encoder output for the decoding steps. However, the transformer's encoder and decoder networks themselves are also based on attention instead of recurrence or convolution: Each layer weights and transforms the previous layer's output in

1160-493: Is used as a "bridge" between two more distant languages. For example, in the case of translating to English from Ukrainian using Russian as an intermediate language. In interlingual machine translation systems, there are two monolingual components: the analysis of the source language and the interlingual, and the generation of the interlingua and the target language. It is however necessary to distinguish between interlingual systems using only syntactic methods (for example

1218-447: Is used during the training phase: The model (the “student” in the teacher forcing metaphor) is always fed the previous ground-truth tokens as input for the next token, regardless of what it predicted in the previous step. As outlined in the history section above, instead of using an NMT system that is trained on parallel text, one can also prompt a generative LLM to translate a text. These models differ from an encoder-decoder NMT system in

SECTION 20

#1732855715333

1276-399: The encoder-decoder architecture: They first use an encoder network to process x {\displaystyle \mathbf {x} } and encode it into a vector or matrix representation of the source sentence. Then they use a decoder network that usually produces one target word at a time, taking into account the source representation and the tokens it previously produced. As soon as

1334-588: The source language into English and then translated the English into the target language rather than translating directly from one language to another. A July 2019 study in Annals of Internal Medicine found that "Google Translate is a viable, accurate tool for translating non–English-language trials". Only one disagreement between reviewers reading machine-translated trials was due to a translation error. Since many medical studies are excluded from systematic reviews because

1392-420: The transfer approach . In the direct approach, words are translated directly without passing through an additional representation. In the transfer approach the source language is transformed into an abstract, less language-specific representation. Linguistic rules which are specific to the language pair then transform the source language representation into an abstract target language representation and from this

1450-516: The zero-shot setting especially for the high-resource language translations". The WMT23 evaluated the same approach (but using GPT-4 ) and found that it was on par with the state of the art when translating into English, but not quite when translating into lower-resource languages. This is plausible considering that GPT models are trained mainly on English text. NMT has overcome several challenges that were present in statistical machine translation (SMT): NMT models are usually trained to maximize

1508-565: The "secretive Google X research lab" by Google Fellow Jeff Dean , Google Researcher Greg Corrado , and Stanford University Computer Science professor Andrew Ng . Ng's work has led to some of the biggest breakthroughs at Google and Stanford. In November 2016, Google Neural Machine Translation system (GNMT) was introduced. Since then, Google Translate began using neural machine translation (NMT) in preference to its previous statistical methods (SMT) which had been used since October 2007, with its proprietary, in-house SMT technology. Training GNMT

1566-464: The 17th century with Descartes and Leibniz , who came up with theories of how to create dictionaries using universal numerical codes, not unlike numerical tokens used by large language models nowadays. Others, such as Cave Beck , Athanasius Kircher and Johann Joachim Becher worked on developing an unambiguous universal language based on the principles of logic and iconographs. In 1668, John Wilkins described his interlingua in his "Essay towards

1624-432: The computing resources of the time were not sufficient to process datasets large enough for the computational complexity of the machine translation problem on real-world texts. Instead, other methods like statistical machine translation rose to become the state of the art of the 1990s and 2000s. During the time when statistical machine translation was prevalent, some works used neural methods to replace various parts in

1682-403: The decoder produces a special end of sentence token, the decoding process is finished. Since the decoder refers to its own previous outputs during, this way of decoding is called auto-regressive . In 1987, Robert B. Allen demonstrated the use of feed-forward neural networks for translating auto-generated English sentences with a limited vocabulary of 31 words into Spanish. In this experiment,

1740-763: The fact that the logarithm of a product is the sum of the factors’ logarithms and flipping the sign yields the classic cross-entropy loss : θ ∗ = a r g m i n θ − ∑ i T log ⁡ ∑ j = 1 J ( i ) P ( y j ( i ) | y 1 , j − 1 ( i ) , x ( i ) ) {\displaystyle \theta ^{*}={\underset {\theta }{\operatorname {arg\,min} }}-\sum _{i}^{T}\log \sum _{j=1}^{J^{(i)}}P(y_{j}^{(i)}|y_{1,j-1}^{(i)},\mathbf {x} ^{(i)})} In practice, this minimization

1798-413: The former became the basis of a commercial system for the transfer of funds, and the latter's code is preserved at The Computer Museum at Boston as the first interlingual machine translation system. In the 1980s, renewed relevance was given to interlingua-based, and knowledge-based approaches to machine translation in general, with much research going on in the field. The uniting factor in this research

Google Neural Machine Translation - Misplaced Pages Continue

1856-740: The goal is finding the model parameters θ ∗ {\displaystyle \theta ^{*}} that maximize the sum of the likelihood of each target sentence in the training data given the corresponding source sentence: θ ∗ = a r g m a x θ ∑ i T P θ ( y ( i ) | x ( i ) ) {\displaystyle \theta ^{*}={\underset {\theta }{\operatorname {arg\,max} }}\sum _{i}^{T}P_{\theta }(\mathbf {y} ^{(i)}|\mathbf {x} ^{(i)})} Expanding to token level yields: θ ∗ =

1914-500: The interlingua can be thought of as a way of describing the analysis of a text written in a source language such that it is possible to convert its morphological, syntactic, semantic (and even pragmatic) characteristics, that is "meaning" into a target language . This interlingua is able to describe all of the characteristics of all of the languages which are to be translated, instead of simply translating from one language to another. Sometimes two interlinguas are used in translation. It

1972-437: The interlingua. The main disadvantage of this strategy is the difficulty of creating an adequate interlingua. It should be both abstract and independent of the source and target languages. The more languages added to the translation system, and the more different they are, the more potent the interlingua must be to express all possible translation directions. Another problem is that it is difficult to extract meaning from texts in

2030-413: The large end-to-end framework, the system learns over time to create better, more natural translations. GNMT attempts to translate whole sentences at a time, rather than just piece by piece. The GNMT network can undertake interlingual machine translation by encoding the semantics of the sentence, rather than by memorizing phrase-to-phrase translations. The Google Brain project was established in 2011 in

2088-532: The likelihood of observing the training data. I.e., for a dataset of T {\displaystyle T} source sentences X = x ( 1 ) , . . . , x ( T ) {\displaystyle X=\mathbf {x} ^{(1)},...,\mathbf {x} ^{(T)}} and corresponding target sentences Y = y ( 1 ) , . . . , y ( T ) {\displaystyle Y=\mathbf {y} ^{(1)},...,\mathbf {y} ^{(T)}} ,

2146-786: The maximum, we can just as well search for the maximum of the logarithm instead (which has the advantage that it avoids floating point underflow that could happen with the product of low probabilities). θ ∗ = a r g m a x θ ∑ i T log ⁡ ∏ j = 1 J ( i ) P ( y j ( i ) | y 1 , j − 1 ( i ) , x ( i ) ) {\displaystyle \theta ^{*}={\underset {\theta }{\operatorname {arg\,max} }}\sum _{i}^{T}\log \prod _{j=1}^{J^{(i)}}P(y_{j}^{(i)}|y_{1,j-1}^{(i)},\mathbf {x} ^{(i)})} Using

2204-436: The most relevant translation. The result is then rearranged and adapted to approach grammatically based human language. GNMT's proposed architecture of system learning was first tested on over a hundred languages supported by Google Translate. GNMT did not create its own universal interlingua but rather aimed at finding the commonality between many languages using insights from psychology and linguistics. The new translation engine

2262-636: The original transformer's decoder is still auto-regressive, which means that decoding still has to be done one token at a time during inference. The transformer model quickly became the dominant choice for machine translation systems and was still by far the most-used architecture in the Workshop on Statistical Machine Translation in 2022 and 2023. Usually, NMT models’ weights are initialized randomly and then learned by training on parallel datasets. However, since using large language models (LLMs) such as BERT pre-trained on large amounts of monolingual data as

2320-401: The prevailing choice in the main machine translation conference Workshop on Statistical Machine Translation. Gehring et al. combined a CNN encoder with an attention mechanism in 2017, which handled long-range dependencies in the source better than previous approaches and also increased translation speed because a CNN encoder is parallelizable, whereas an RNN encoder has to encode one token at

2378-409: The problems of knowledge-based machine translation systems is that it becomes impossible to create databases for domains larger than very specific areas. Another is that processing these databases is very computationally expensive. One of the main advantages of this strategy is that it provides an economical way to make multilingual translation systems. With an interlingua it becomes unnecessary to make

Google Neural Machine Translation - Misplaced Pages Continue

2436-552: The reviewers do not understand the language, GNMT has the potential to reduce bias and improve accuracy in such reviews. As of December 2021, all of the languages of Google Translate support GNMT, with Latin being the most recent addition. Neural machine translation It is the dominant approach today and can produce translations that rival human translations when translating between high-resource languages under specific conditions. However, there still remain challenges, especially with languages where less high-quality data

2494-424: The size of the network's input and output layers was chosen to be just large enough for the longest sentences in the source and target language, respectively, because the network did not have any mechanism to encode sequences of arbitrary length into a fixed-size representation. In his summary, Allen also already hinted at the possibility of using auto-associative models, one for encoding the source and one for decoding

2552-406: The source language is to be translated into a sentence y = x 1 , J {\displaystyle \mathbf {y} =x_{1,J}} (consisting of J {\displaystyle J} tokens x j {\displaystyle x_{j}} ) in the target language. The source and target tokens (which in the simple event are used for each other in order for

2610-605: The source sentence and the previously predicted target tokens. The probability of the whole translation then is the product of the probabilities of the individual predicted tokens: P ( y | x ) = ∏ j = 1 J P ( y j | y 1 , i − 1 , x ) {\displaystyle P(y|x)=\prod _{j=1}^{J}P(y_{j}|y_{1,i-1},\mathbf {x} )} NMT models differ in how exactly they model this function P {\displaystyle P} , but most use some variation of

2668-413: The state of the decoder is used to calculate a source representation that focuses on different parts of the source and uses that representation in the calculation of the probabilities for the next token. Based on these RNN-based architectures, Baidu launched the "first large-scale NMT system" in 2015, followed by Google Neural Machine Translation in 2016. From that year on, neural models also became

2726-443: The statistical machine translation while still using the log-linear approach to tie them together. For example, in various works together with other researchers, Holger Schwenk replaced the usual n-gram language model with a neural one and estimated phrase translation probabilities using a feed-forward network. In 2013 and 2014, end-to-end neural machine translation had their breakthrough with Kalchbrenner & Blunsom using

2784-431: The system had been replaced by another deep learning system based on a Transformer encoder and an RNN decoder. GNMT improved on the quality of translation by applying an example-based (EBMT) machine translation method in which the system learns from millions of examples of language translation. GNMT's proposed architecture of system learning was first tested on over a hundred languages supported by Google Translate. With

2842-556: The systems developed in the 1970s at the universities of Grenoble and Texas) and those based on artificial intelligence (from 1987 in Japan and the research at the universities of Southern California and Carnegie Mellon). The first type of system corresponds to that outlined in Figure 1. while the other types would be approximated by the diagram in Figure 4. The following resources are necessary to an interlingual machine translation system: One of

2900-558: The target sentence is generated. The interlingual approach to machine translation has advantages and disadvantages. The advantages are that it requires fewer components in order to relate each source language to each target language, it takes fewer components to add a new language, it supports paraphrases of the input in the original language, it allows both the analysers and generators to be written by monolingual system developers, and it handles languages that are very different from each other (e.g. English and Arabic ). The obvious disadvantage

2958-456: The target. Lonnie Chrisman built upon Allen's work in 1991 by training separate recursive auto-associative memory (RAAM) networks (developed by Jordan B. Pollack ) for the source and the target language. Each of the RAAM networks is trained to encode an arbitrary-length sentence into a fixed-size hidden representation and to decode the original sentence again from that representation. Additionally,

SECTION 50

#1732855715333

3016-419: The translation task. Instead of fine-tuning a pre-trained language model on the translation task, sufficiently large generative models can also be directly prompted to translate a sentence into the desired language. This approach was first comprehensively tested and evaluated for GPT 3.5 in 2023 by Hendy et al. They found that "GPT systems can produce highly fluent and competitive translation outputs even in

3074-578: The two networks are also trained to share their hidden representation; this way, the source encoder can produce a representation that the target decoder can decode. Forcada and Ñeco simplified this procedure in 1997 to directly train a source encoder and a target decoder in what they called a recursive hetero-associative memory . Also in 1997, Castaño and Casacuberta employed an Elman's recurrent neural network in another machine translation task with very limited vocabulary and complexity. Even though these early approaches were already similar to modern NMT,

3132-447: Was a big effort at the time and took, by a 2021 OpenAI estimate, on the order of 100 PFLOP/s*day (up to 10 FLOPs) of compute which was 1.5 orders of magnitude larger than Seq2seq model of 2014 (but about 2x smaller than GPT-J-6B in 2021). Google Translate's NMT system uses a large artificial neural network capable of deep learning . By using millions of examples, GNMT improves the quality of translation, using broader context to deduce

3190-427: Was added for nine Indian languages: Hindi, Bengali, Marathi, Gujarati, Punjabi, Tamil, Telugu, Malayalam and Kannada at the end of April 2017. By 2020, Google had changed methodology to use a different neural network system based on transformers , and had phased out NMT. The GNMT system was said to represent an improvement over the former Google Translate in that it will be able to handle "zero-shot translation", that

3248-581: Was discussed extensively by the Israeli philosopher Yehoshua Bar-Hillel in 1969. During the 1970s, noteworthy research was done in Grenoble by researchers attempting to translate physics and mathematical texts from Russian to French , and in Texas a similar project (METAL) was ongoing for Russian to English . Early interlingual MT systems were also built at Stanford in the 1970s by Roger Schank and Yorick Wilks ;

3306-601: Was first enabled for eight languages: to and from English and French, German, Spanish, Portuguese, Chinese, Japanese, Korean and Turkish in November 2016. In March 2017, three additional languages were enabled: Russian, Hindi and Vietnamese along with Thai for which support was added later. Support for Hebrew and Arabic was also added with help from the Google Translate Community in the same month. In mid April 2017 Google Netherlands announced support for Dutch and other European languages related to English. Further support

3364-610: Was that high-quality translation required abandoning the idea of requiring total comprehension of the text. Instead, the translation should be based on linguistic knowledge and the specific domain in which the system would be used. The most important research of this era was done in distributed language translation (DLT) in Utrecht , which worked with a modified version of Esperanto , and the Fujitsu system in Japan. In 2016, Google Neural Machine Translation achieved "zero-shot translation", that

#332667