Machine Translation
by Thierry Poibeau · 14 Sep 2017 · 174pp · 56,405 words
the Modern Corporation, James W. Cortada Intellectual Property Strategy, John Palfrey The Internet of Things, Samuel Greengard Machine Learning: The New AI, Ethem Alpaydin Machine Translation, Thierry Poibeau Memes in Digital Culture, Limor Shifman Metadata, Jeffrey Pomerantz The Mind–Body Problem, Jonathan Westphal MOOCs, Jonathan Haber Neuroplasticity, Moheb Costandi Open
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Self-Tracking, Gina Neff and Dawn Nafus Sustainability, Kent E. Portney The Technological Singularity, Murray Shanahan Understanding Beliefs, Nils J. Nilsson Waves, Frederic Raichlen Machine Translation Thierry Poibeau The MIT Press Cambridge, Massachusetts London, England © 2017 Massachusetts Institute of Technology All rights reserved. No part of this book may be reproduced
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Report and Its Consequences 7 Parallel Corpora and Sentence Alignment 8 Example-Based Machine Translation 9 Statistical Machine Translation and Word Alignment 10 Segment-Based Machine Translation 11 Challenges and Limitations of Statistical Machine Translation 12 Deep Learning Machine Translation 13 The Evaluation of Machine Translation Systems 14 The Machine Translation Industry: Between Professional and Mass-Market Applications 15 Conclusion: The Future of
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Machine Translation Glossary Bibliography and Further Reading Index About Author List of Tables Table 1 Example of possible
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from the analysis of knowledge and reasoning, which explains the interest shown by philosophers and specialists of artificial intelligence as well as cognitive sciences in [machine translation]. Machine translation involves different processes that make it at least as challenging as developing an automatic dialoguing system. The degree of “understanding” shown by the machine
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inferred information, which is of course highly challenging and simply goes beyond the current state of the art. The Revolution of Statistical Machine Translation Systems The classification of machine translation systems provided in the previous section is challenged by new approaches that have appeared since the early 1990s. The availability of huge quantities
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in such symbols could be translated by all who possessed the dictionary” (Descartes, letter to Mersenne on November 20, 1629). This passage greatly inspired machine translation pioneers, since Descartes’ proposal aimed to replace words with unambiguous codes (“symbols” corresponding to numerical codes that are independent from the languages considered; symbols
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in automatic translation systems. Although these projects resulted in relatively advanced proposals with vocabularies and grammar systems, they have rarely been actively used for machine translation. Esperanto was used during the 1980s in the European Distributed Translation Language project and within the Fujitsu company in Japan, but these two projects
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it must therefore be translated; in other words, decoded in the target language). Beginning in 1947, Weaver corresponded with the cyberneticist Norbert Wiener concerning machine translation. He proposed that translation could be considered a “decoding” problem: One naturally wonders if the problem of translation could conceivably be treated as a problem
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more powerful than symbolic rules to resolve ambiguities). The implementation of the proposed techniques, however, required efforts that went beyond anything the pioneers of machine translation had ever imagined. In particular, the inherent ambiguity of natural languages showed that traditional encryption models were not sufficient to render the complexity of automatic
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-quality translation in the short or medium term (FAHQT, or fully automated high-quality translation; also found as FAHQMT, or fully automated high-quality machine translation). Instead of automatic translation, Bar-Hillel recommended that researchers turn toward computer-assisted translation systems, which constitute a relatively different project, clearly less exciting scientifically
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the other hand, with the increasing amount of translations available on the Internet, it is now possible to directly design statistical models for machine translation. This approach, known as statistical machine translation, is the most popular today. Unlike a translation memory, which can be relatively small, automatic processing presumes the availability of an
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it would be more convenient to directly use fragments of translation that one can find in existing bilingual corpora. An Overview of Example-Based Machine Translation Example-based machine translation typically operates in three stages to translate a given sentence: The system tries to find fragments of the sentence to be translated in the
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sparsity (it is very difficult to collect enough relevant examples at phrase level). Appeal and Limitations of Example-Based Machine Translation Example-based machine translation generated great interest during the 1980s. Rather than developing a machine translation system manually, which is long and very costly, the example-based approach allowed for optimal exploitation of large
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information of a syntactic and semantic nature has been progressively integrated into models to compensate for the limitation of purely statistical approaches. Toward Segment-Based Machine Translation The IBM models have been subjected to numerous enhancements. The most significant improvement was to take into consideration the notion of segments (or sequences
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have described in this section have, however, helped improve the IBM models and can still be considered currently as the state of the art in machine translation. Introduction of Linguistic Information into Statistical Models Statistical translation models, despite their increasing complexity to better fit language specificities, have not solved all the
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techniques. Lastly, for rare languages with too few data to make it possible to develop statistical systems, rule-based systems remain the norm. Hybrid Machine Translation Systems Following the success of statistical translation systems, the majority of traditional systems (based on large lexicons and transfer rules) gradually tried to incorporate statistical
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deep learning provides an interesting approach that seems especially fitted for the challenges involved in improving human language processing. An Overview of Deep Learning for Machine Translation Deep learning achieved its first success in image recognition. Rather than using a group of predefined characteristics, deep learning generally operates from a very
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to let the system infer by itself the best representation from the data. A translation system based solely on deep learning (aka “deep learning machine translation” or “neural machine translation”) thus simply consists of an “encoder” (the part of the system that analyzes the training data) and a “decoder” (the part of the
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toward the resolution of such problems, hence the great success of this technique among researchers in the domain. Current Challenges for Deep Learning Machine Translation Until recently, machine translation systems based on deep learning performed well on simple sentences but still lagged behind traditional statistical systems for more complex sentences. There were different
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of the internal model calculated by the neural network, so as to better understand how the whole approach works. The deep learning approach to machine translation (or neural machine translation) has proven efficient, first, on short sentences in closely related languages, and more recently on long sentences as well as more diverse languages.
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initiated research in this area. A 1994 article (White et al., 1994) reviewed the first attempts at evaluation from the beginnings of research on machine translation. The article specifically reported the various possible strategies and their limits, described below. Comprehension Evaluation To assess comprehension, professional human translators first translated English newspaper
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articles into different languages. Machine translation systems then translated the text back into English, and human analysts answered “multiple choice questions about the content of the articles” to evaluate the automatic
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Fluency After the previous attempts involving human experts, DARPA then resorted to two evaluation scores: adequacy and fluency. As White and colleagues described of this machine translation (MT) evaluation method: “In an adequacy evaluation, literate, monolingual English speakers make judgments determining the degree to which the information in a professional translation
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automatically. It is, rather, the capacity of the system to provide relevant translational elements that should be evaluated. 14 The Machine Translation Industry: Between Professional and Mass-Market Applications Machine translation is a popular application because it answers a very direct and simple need. Everybody can clearly see the importance of a system
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analysis and translation systems used by Samsung’s connected devices (cell phones, tablets, and other technological gadgets). Facebook bought out different companies specialized in machine translation (such as Jibbigo in 2013 for voice messages in particular). Apple and Google are also regularly buying startups in the communication and information technology domains
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is actually twofold. First, improving the productivity of translators: this involves efficient systems and strategies to make the best of the output of machine translation tools. Second, improving machine translation systems directly: this means being able to dynamically reuse end-user feedback to make the system evolve and propose more accurate translations in
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Warren Weaver and the launching of MT: Brief biographical note”) and Y. Bar-Hillel (“Yehoshua Bar-Hillel: A philosopher’s contribution to machine translation”), both in Early Years in Machine Translation (see the full reference at the beginning of this chapter). Chapter 6: The 1966 ALPAC Report and Its Consequences The ALPAC report and
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previous website (http://www.statmt.org) is probably the best source of information for recent trends related to statistical machine translation, of which segment-based machine translation is part. Chapter 11: Challenges and Limitations of Statistical Machine Translation See http://www.statmt.org,as for chapter 10 above. Kenneth Church (2011). “A pendulum swung too
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Journal of Translation Studies 13 (1–2): 29–70. Special issue: The teaching of computer-aided translation, ed. Chan Sin Wai. Philipp Koehn (2009). Statistical Machine Translation. Cambridge: Cambridge University Press. Jorg Tiedemann (2011). Bitext Alignment. San Rafael, CA: Morgan and Claypool Publishers. Dan Jurafsky and James H. Martin (2016). Speech
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3: The Correspondence. Cambridge: Cambridge University Press. Umberto Eco (1997). The Search for the Perfect Language. Oxford: Wiley. John Hutchins (2004). “Two precursors of machine translation: Artsrouni and Trojanskij.” International Journal of Translation 16 (1): 11–31. Philip P. Wiener (ed., 1951). Leibniz Selections. New York: Simon and Schuster. Yehoshua Bar
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Human Intelligence (A. Elithorn and R. Banerji, eds.). Elsevier Science Publishers, Amsterdam. Eiichiro Sumita and Hitoshi Iida (1991). “Experiments and prospects of example-based machine translation.” Proceedings of the Twenty-Ninth Conference of the Association for Computational Linguistics, 185–192. Berkeley, CA. Thomas R. Green (1979). “The necessity of syntax markers
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: Two experiments with artificial languages.” Verbal Learning and Verbal Behavior 18: 481–496. Harold Somers (1999). “Example-based machine translation.” Machine translation 14 (2): 113–157. Nano Gough and Andy Way (2004). “Robust large-scale EBMT with marker-based segmentation.” Proceedings of the Tenth International Conference on
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Theoretical and Methodological Issues in Machine Translation, 95–104. Baltimore, MD. Peter Brown, John Cocke, Stephen Della Pietra, Vincent Della Pietra, Frederick Jelinek, Robert Mercer, and Paul Roossin (1988). “A
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, Yoshua Bengio and Aaron Courville (2016). Deep Learning. Cambridge, MA: MIT Press. Yonghui Wu, et al. (2016). “Google's neural machine translation system: Bridging the gap between human and machine translation.” Published online. arXiv:1609.08144. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu (2002). “BLEU: A method for automatic
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51, 56, 61, 63, 100, 118, 164–168, 215–216, 250 Environment Canada, 87, 223 Error rate, 241. See also Evaluation Errors (in machine translation). See Typology of errors in machine translation Escher, M. C., 20 Esperanto, 28, 42, 44 Estonian, 97, 212, 213 Europarl corpus, 97, 210, 223 Europe, 36, 41, 86, 97
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58, 60, 85, 179 Logos Corporation, 88 Machine learning, 175, 181, 183, 236. See also Deep learning Machine translation evaluation. See Evaluation Machine translation industry. See Machine translation market Machine translation market, 89, 221–246, 247–251 Machine translation quality. See Evaluation Machine translation systems Apertium, 172 Ariane-78 system, 85 Babelfish, 227, 228 Bing Translation, 33, 36, 194, 226–229,
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Systran) TAUM Météo (see Météo) Watson, 241 Maintenance applications, 243 Maltese, 212, 213 Manual correction, 138. See also Post-edition Mass-market applications of machine translation. See Machine translation market Mass media, 239 Mathematical model of communication. See Model of communication Meaning, 8, 15, 17–21, 34, 52–55, 64–67, 70–71
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, Igor, 69 Memorandum, Warren Weaver’s, 50, 52–59 Mercer, Robert, 93, 94, 166, 216, 258 Mersel, Jules, 76 Metal. See Machine translation systems Metaphysics, 179 Météo. See Machine translation systems Meteor. See Evaluation measure and test Michigan University, 81 Microsoft, 227–229, 240, 248–250 MIT, 60–62 Mobile application, 229, 232
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264 System comparison (see Evaluation) maintenance, 243 quality (see Evaluation) Systran, 85–89, 171, 194, 223, 226, 227–229, 231–236 Systranet. See Machine translation systems TAUM Météo. See Machine translation systems Technical text, 4, 11, 13, 88, 223, 244 Technical translation, 92. See also Technical text Terminology, 92, 101, 119, 226, 228, 244
The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence
by Sebastian Mallaby; · 30 Mar 2026 · 607pp · 161,998 words
PhD of Koray Kavukcuoglu and was a coauthor with Karol Gregor. BACK TO NOTE REFERENCE 13 Neural networks delivered substantial improvements in medical diagnostics and machine translation from 2016. That year, researchers at Stanford demonstrated machine diagnosis of skin cancer to be as accurate as that achieved by human dermatologists. Also in
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2016, Google introduced its Neural Machine Translation system, delivering a big jump in performance. BACK TO NOTE REFERENCE 14 Mnih, author interview. BACK TO NOTE REFERENCE 15 David Silver, author interview, December
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only you could train them!’ ” Ilya Sutskever, author interview, November 3, 2024. BACK TO NOTE REFERENCE 15 Dzmitry Bahdanau, KyungHyun Cho, and Yoshua Bengio, “Neural Machine Translation by Jointly Learning to Align and Translate,” arXiv, May 19, 2016, arxiv.org/abs/1409.0473. “Attention” is mentioned only three times in the paper
The Age of AI: And Our Human Future
by Henry A Kissinger, Eric Schmidt and Daniel Huttenlocher · 2 Nov 2021 · 194pp · 57,434 words
of language did not reduce to simple rules. All this changed when, in 2015, developers began to apply deep neural networks to the problem. Suddenly, machine translation leaped forward. But its improvement did not just derive from the application of neural networks or machine-learning techniques. Rather, it sprang from new and
Kingdom of Characters: The Language Revolution That Made China Modern
by Jing Tsu · 18 Jan 2022 · 408pp · 105,715 words
the time, however, the Cold War had begun and the United States and the Soviet Union were racing to make advances in cryptography research and machine translation, the automated translation of human languages by machines, one of the first areas of research in artificial intelligence. Both superpowers saw clearly that whoever controlled
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control the future of information. After Mergenthaler bought the rights from Lin, the U.S. Air Force acquired the keyboard in an effort to study machine translation and disk storage for rapid access to large quantities of information. Chinese had been identified as one of the priority languages of study. The USAF
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IBM research center in upstate New York. King later moved to Itek, a defense contractor in Massachusetts, where he coauthored a seminal scientific paper on machine translation. He also unveiled the machine they built as a result of studying Lin’s keyboard—the Sinowriter, a device for converting Chinese-character texts into
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information of a character, the more useful that code can be. These extensions of Zhi’s system would be important for Chinese-language applications in machine translation and retrieving information from stored data. Zhi formally introduced his “On-Sight” encoding system in the Chinese science journal Nature Magazine in 1978. He described
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that GARF said it was. In the end, the Pentagon decided that it was not lethal enough as a weapon of propaganda war. Technologies like machine translation for deciphering Russian or Chinese documents or cryptography were of greater consequence for America’s strategic interests. When Caldwell died suddenly two months after his
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. In college, he came across the 1963 article by Gilbert W. King that drew on Lin Yutang’s typewriter keyboard to discuss the possibility of machine translation. Although Becker did not know who Lin was, let alone how his keyboard connected the United States to China’s efforts to modernize its script
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has landed China here, at the beginning—not the end—of becoming a standard setter, from artificial intelligence to quantum natural language processing, automation to machine translation. The Chinese script has completely turned around its position in relation to the Western alphabetic script. There are currently more than 900 million internet users
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by 2035. Deep neural networks are being trained on China’s ever-growing volume of data. Chinese tech giant Baidu has become a leader in machine translation and natural language processing, while Tencent sits on a wealth of data gathered through WeChat and its video gaming platforms. From health care to smart
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: Guangdong jiaoyu, 2012), pp. 151−59. GO TO NOTE REFERENCE IN TEXT The USAF handed Lin’s keyboard: W. John Hutchins, ed., Early Years in Machine Translation: Memoirs and Biographies of Pioneers (Amsterdam, Philadelphia: J. Benjamins, 2000), pp. 21−72, 171−76. GO TO NOTE REFERENCE IN TEXT five: when “peking” became
The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do
by Erik J. Larson · 5 Apr 2021
labs at MIT, Stanford, and elsewhere were encountering seemingly endless quandaries, difficulties, confusions, and outright failures. The problem, for example, of Fully Automated High-Quality Machine Translation was thought in the 1950s to be solvable, given sufficient research effort and dollars. By the 1960s, government investment in translation dried up, on the
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achieved general intelligence, and thus be capable of thinking and acting like humans. By the 1960s, therefore, the target of AI was the task of machine translation—the fully automatic rendering of texts from one language, such as Russian, into another, such as English. AI was “all in.” NATURAL LANGUAGE UNDERSTANDING As
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Man can do.” Marvin Minsky, too, declared in 1967 that “within a generation, the problem of creating ‘artificial intelligence’ will be substantially solved.”2 But machine translation was a different ballgame, as researchers soon discovered. Having begun with a simplistic assumption, that language could be understood by analyzing words in large texts
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high quality. Even programs working in specific domains such as biomedical literature were not fail-proof, and the failures were often embarrassingly incorrect and mindless. Machine translation researchers, in response, expanded their approach by exploring methods for “parsing” sentences, or finding syntactic structure in them, using new and powerful “transformational” grammars developed
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had walked into a game of three-dimensional chess thinking it was tic-tac-toe.3 The National Resource Council (NRC) was pouring millions into machine-translation work at a number of American universities by the mid-1960s, but as for actual successes in engineering systems to understand, or even to simulate
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the depth of problems the field faced.4 The effects of his reports on the research community were seismic. He had pinpointed the exact obstacle machine translation was foundering on, and it was irritatingly “philosophical”: the dearth of so-called common sense or “world knowledge”—knowledge about the actual world. Consider a
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the NRC, the notion that computers could be programmed with the world knowledge of humans was “utterly chimerical, and hardly deserves any further discussion.”6 Machine translation was stuck, in other words, with results that were a far cry from fully automatic, high-quality translations (and that remain so today, although the
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to data-driven or “empirical” methods seemed to liberate AI from those early, cloudy days of work on machine translation, when seemingly endless problems with capturing meaning and context plagued engineering efforts. In fact, machine translation itself was later cracked by a group of IBM researchers using a statistical (that is, not grammar-based
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intensive—statistical approach pioneered by IBM Research Labs. SUCCESS … OR NOT The success of contemporary systems like Google Translate on the once puzzling problem of machine translation is often touted as evidence that AI will succeed, given enough time and the right ideas. The truth is more sobering. While it turns out
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statistical or machine learning approaches, the original concerns of Bar-Hillel and others regarding semantics (meaning) and pragmatics (context) have proven to be well-founded. Machine translation, which had seemed like a difficult natural language problem, could be adequately accomplished with simple statistical analysis, given large corpora (datasets) in different languages. (And
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note that machine translation is still not very high quality—it is more like “good enough to be useful.”) This is not evidence of impressive growth in machines’ natural
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language understanding intelligence, but only evidence that machine translation is a much simpler problem than it was initially perceived to be. Again, deep problems with understanding language using computers have persisted. A simple way
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MYCIN, which provided sometimes quite good medical diagnoses, made clear that AI methods were relevant to a variety of problems normally requiring high human intelligence. Machine translation, as we’ve seen, was an initial failure, but yielded to different approaches made possible by the availability of large datasets (a precursor to many
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. Did the bot trick some judges some of the time? Yes. Did it actually pass the Turing test in any meaningful way? No.10 Though machine translation has in recent years been tamed (somewhat) by large volumes of texts translated into different languages on the web, the Turing test remains a perpetual
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in the pen or I loved the river. I walked to the bank.) Confusingly, work in the 1960s on so-called fully-automated high-quality machine translation began with statistical approaches, albeit simpler ones. They didn’t work very well, and soon apostates like Yehoshua Bar-Hillel concluded that automatic translation was
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IBM researchers, and web giants like Google used similar techniques (and now deep learning) to provide decent statistically-driven translation services. Fully-automated high-quality machine translation came full circle. But Bar-Hillel’s original skepticism is still germane; translations requiring knowledge and context are still a black box to modern approaches
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Research Council, Publication 1416, 1966. 5. Sergei Nirenburg, H. L. Somers, and Yorick Wilks, eds., Readings in Machine Translation (Cambridge, MA: MIT Press, 2003), 75. 6. For a readable discussion of early problems with machine translation, see John Haugeland, Artificial Intelligence, The Very Idea (Cambridge, MA: MIT Press, 1989). Yehoshua Bar-Hillel’s comment
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formal systems, 284n6 Frankenstein (fictional character), 238 Frankenstein: Or, a Modern Prometheus (novel, Shelly), 238, 280 frequency assumptions, 150–154, 173 Fully Automated High-Quality Machine Translation, 48 functions, 139 Galileo, 160 gambler’s fallacy, 122 games, 125–126 Gardner, Dan, 69–70 Garland, Alex, 79, 80, 289n16 Gates, Bill, 75 general
Artificial Intelligence: A Modern Approach
by Stuart Russell and Peter Norvig · 14 Jul 2019 · 2,466pp · 668,761 words
, the MNIST data set for handwritten digit recognition, ImageNet and COCO for image object recognition, SQUAD for natural language question answering, the WMT competition for machine translation, and the International SAT Competitions for Boolean satisfiability solvers. AI was founded in part as a rebellion against the limitations of existing fields like control
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learning systems have exceeded human performance on some vision tasks (and lag behind in some other tasks). Similar gains have been reported in speech recognition, machine translation, medical diagnosis, and game playing. The use of a deep network to represent the evaluation function contributed to ALPHAGO’S victories over the leading human
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Google Maps provide driving directions for hundreds of millions of users, quickly plotting an optimal route taking into account current and predicted future traffic conditions. Machine translation: Online machine translation systems now enable the reading of documents in over 100 languages, including the native languages of over 99% of humans, and render hundreds of
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for HMMs first appeared in Russell and Norvig (2003). HMMs have found many applications in language processing (Charniak, 1993), speech recognition (Rabiner and Juang, 1993), machine translation (Och and Ney, 2003), computational biology (Krogh et al., 1994; Baldi et al., 1994), financial economics (Bhar and Hamori, 2004) and other fields. There have
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model that makes you $10 on every trade, that’s great—but not if it costs you $20 in computation cost for each prediction. A machine translation program that runs on your phone and allows you to read signs in a foreign city is helpful—but not if it runs down the
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computation paths from inputs to outputs have many steps. Deep learning is currently the most widely used approach for applications such as visual object recognition, machine translation, speech recognition, speech synthesis, and image synthesis; it also plays a significant role in reinforcement learning applications (see Chapter 23). Deep learning has its origins
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algorithm for each experiment. This has encouraged an approach called end-to-end learning, in which a complex computational system for a task such as machine translation can be composed from several trainable subsystems; the entire system is then trained in an end-to-end fashion from input/output pairs. With this
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translation tasks. Supervised translation consists of gathering many (x, y) pairs and training the model to map each x to the corresponding y. For example, machine translation systems are often trained on pairs of sentences that have been translated by professional human translators. For other kinds of translation, supervised training data may
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with near-perfect accuracy. 22.8.2Natural language processing Deep learning has also had a huge impact on natural language processing (NLP) applications such as machine translation and speech recognition. Some advantages of deep learning for these applications include the possibility of end-to-end learning, the automatic generation of internal representations
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encoders and decoders. End-to-end learning refers to the construction of entire systems as a single, learned function f. For example, an f for machine translation might take as input an English sentence SE and produce an equivalent Japanese sentence SJ = f (SE). Such an f can be learned from training
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. (2016b) showed that end-to-end translation using deep learning reduced translation errors by 60% relative to a previous pipeline-based system. As of 2020, machine translation systems are approaching human performance for language pairs such as French and English for which very large paired data sets are available, and they are
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for image processing and other tasks where the data have a grid topology. •Recurrent networks are effective for sequence-processing tasks including language modeling and machine translation. Bibliographical and Historical Notes The literature on neural networks is vast. Cowan and Sharp (1988b, 1988a) survey the early history, beginning with the work of
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about 2/3 of listeners saying that the neural WaveNet system (van den Oord et al., 2016a) sounded more natural than the previous nonneural system. Machine translation transforms text in one language to another. Systems are usually trained using a bilingual corpus: a set of paired documents, where one member of the
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pair is in, say, English, and the other is in, say, French. The documents do not need to be annotated in any way; the machine translation system learns to align sentences and phrases and then when presented with a novel sentence in one language, can generate a translation to the other
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covers the use of recurrent neural networks to capture meaning and long-distance context as text is processed sequentially. Section 25.3 focuses primarily on machine translation, one of the major successes of deep learning applied to NLP. Sections 25.4 and 25.5 cover models that can be trained from large
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sentences with many intervening words between the subject and verb. 25.3Sequence-to-Sequence Models One of the most widely studied tasks in NLP is machine translation (MT), where the goal is to translate a sentence from a source language to a target language—for example, from Spanish to English. We train
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a basic sequence-to-sequence model, an example of which is shown in Figure 25.6. Sequence-to-sequence models are most commonly used for machine translation, but can also be used for a number of other tasks, like automatically generating a text caption from an image, or summarization: rewriting a long
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the target RNN. Unlike most components of neural networks, attention probabilities are often interpretable by humans and intuitively meaningful. For example, in the case of machine translation, the attention probabilities often correspond to the word-to-word alignments that a human would generate. This is shown in Figure 25.7(b). Sequence
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-to-sequence models are a natural for machine translation, but almost any natural language task can be encoded as a sequence-to-sequence problem. For example, a question-answering system can be trained on
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called the transformer encoder. It is useful for text classification tasks. The full transformer architecture was originally designed as a sequence-to-sequence model for machine translation. Therefore, in addition to the encoder, it also includes a transformer decoder. The encoder and decoder are nearly identical, except that the decoder uses a
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be used to train a question-answering system. Similarly, many Web sites publish side-by-side translations of texts, which can be used to train machine translation systems. Some text even comes with labels of a sort, such as review sites where users annotate their text reviews with a 5-star rating
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. If this model is trained on a large corpus of text, it generates pretrained representations that perform well across a wide variety of NLP tasks (machine translation, question answering, summarization, grammaticality judgments, and others). 25.6State of the art Deep learning and transfer learning have markedly advanced the state of the art
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can effectively model local and long-distance context by retaining relevant information in their hidden-state vectors. •Sequence-to-sequence models can be used for machine translation and text generation problems. •Transformer models use self-attention and can model long-distance context as well as local context. They can make effective use
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) while maintaining high accuracy. The XLM system (Lample and Conneau, 2019) is a transformer model with training data from multiple languages. This is useful for machine translation, but also provides more robust representations for monolingual tasks. Two other important systems, GPT-2 (Radford et al., 2019) and T5 (Raffel et al., 2019
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France the same time zone as the UK?”) and two reading comprehension tasks involving answering questions after reading either a paragraph or a news article. Machine translation is a major application of language models. In 1933, Petr Troyanskii received a patent for a “translating machine,” but there were no computers available to
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computing resources to make the approach practical. In the 1970s that began to change, and the SYSTRAN system (Toma, 1977) was the first commercially successful machine translation system. SYSTRAN relied on lexical and grammatical rules hand-crafted by linguists as well as on training data. In the 1980s, the community embraced purely
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but not fluent results (Och and Ney, 2004; Zollmann et al., 2008). Och and Ney (2002) show how discriminative training led to an advance in machine translation in the early 2000s. Sutskever et al. (2015) first showed that it is possible to learn an end-to-end sequence-to-sequence neural model
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for machine translation. Bahdanau et al. (2015) demonstrated the advantage of a model that jointly learns to align sentences in the source and target language and to translate
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between the languages. Vaswani et al. (2018) showed that neural machine translation systems can further be improved by replacing LSTMS with transformer architectures, which use the attention mechanism to capture context. These neural translation systems quickly overtook
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workers face, and free them to concentrate on more interesting aspects. People with disabilities will benefit from AI-based assistance in seeing, hearing, and mobility. Machine translation already allows people from different cultures to communicate. Software-based AI solutions have near zero marginal cost of production, and so have the potential to
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, IBM, Salesforce) have begun competing to offer machine learning APIs with pre-built models for specific tasks such as visual object recognition, speech recognition, and machine translation. These models can be used as is, or can serve as a baseline to be customized with your particular data for your particular application. We
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adversarial networks (GANs) and transformer language models each opened up new areas of research. We have also seen steps towards “diversity of behaviour.” For example, machine translation systems in the 1990s were built one at a time for each language pair (such as French to English), but today a single system can
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systematic comparison of various statistical alignment models. Computational Linguistics, 29, 19–51. Och, F. J. and Ney, H. (2004). The alignment template approach to statistical machine translation. Computational Linguistics, 30, 417–449. Och, F. J. and Ney, H. (2002). Discriminative training and maximum entropy models for statistical
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outbreaks a week sooner than the Centers for Disease Control and Prevention. This was Google’s way: it approached classic hard problems of artificial intelligence—machine translation and voice recognition—not with human experts, not with dictionaries and linguists, but with its voracious data mining of trillions of words in more than
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