Index

• Adamic language, 40
• Adams, Douglas, 1, 256
• Adequacy. See Evaluation measure and test
• Advertisement, 226, 229, 232
• Aeronautic industry, 243, 250
• Agglutinative language, 214–216, 261
• Agreement (linguistic), 175
• Aligned texts. See Parallel corpus
• ALPAC Report, 35, 75–83, 199
• AlphaGo, 182
• AltaVista, 227
• Ambiguity, 15–18, 21, 23, 40, 56–59, 64–65, 72, 178, 239, 252, 261
• American defense agencies, 77, 88. See also Defense industry
• American intelligence agencies. See American defense agencies
• Analogy. See Example-based machine translation
• Analytical language, 215–216
• Android, 240
• Apertium. See Machine translation systems
• Apresjan, Yuri 69
• Arabic, 56, 164, 165, 192, 209, 228, 250
• Ariane-78 system. See Machine translation systems
• ARPA, 200, 259. See also DARPA
• Artificial dialogue, 2, 4, 240–241
• Artificial intelligence, 2, 76, 84, 236, 251
• Artificial language, 28, 40, 42–44, 58, 63. See also Esperanto; Volapuk
• Artificial neural network. See Neural network
• Artsrouni, Georges, 45–48, 51
• Asian languages, 88, 105, 114, 117
• Asymmetric alignment, 104–105, 122, 125
• AT&T, 241
• Attention mechanism, 190–192
• Automatic captioning, 239–240
• Automatic evaluation measure. See Evaluation measure and test
• Automatic Language Processing Advisory Committee (ALPAC), 76. See also ALPAC Report
• Automatic subtitling, 239–240

• Babel, 1, 40
• Babelfish. See Machine translation systems
• Babel fish, 1, 255
• Banerjee, Satanjeev, 206
• Bar-Hillel, Joshua, 60–61, 66, 69–74, 75, 81–83, 246
• Basque, 172
• Bayes’ theorem, 127
• Becher, Johann Joachim, 41
• Beck, Cave, 41
• Bilingual corpus. See Parallel corpus
• Bilingual dictionary, 30, 33, 49, 51, 62, 63, 68, 85, 86, 92, 95, 110, 121, 125, 130–131, 172–174, 210, 232–235
• Bilingual lexicon. See Bilingual dictionary
• Bing Translation. See Machine translation systems
• Birkbeck College, 50
• Bi-text. See Parallel corpus
• BLEU. See Evaluation measure and test
• Blog, 167–168, 248
• Booth, Andrew, 50–51
• Brain. See Cognitive plausibility
• Breton, 172
• Brown, Peter F., 126–144

• Cambridge Language Research Unit, 66–67
• Canada, 69, 84–85, 87, 223, 244
• Canadian parliamentary debates. See Hansard corpus
• Carnap, Rudolf, 60
• Case (linguistic), 18, 118, 214
• Cell phone. See Mobile phone
• Centre d’Études sur la Traduction Automatique (CETA), 67–68, 84
• Centre National de la Recherche Scientifique (CNRS), 67
• Chandioux, John, 87
• Child language acquisition, 255
• China, 67, 86
• Chinese, 56, 88, 163–165, 192, 209, 215, 228, 232, 250
• Chomsky, Noam, 63, 65
• Church, Kenneth, 105
• Co-construction of meaning, 20
• Cognate, 11, 107–108, 261
• Cognitive plausibility, 20, 23, 178, 181–184, 187, 251–256
• Cognitive sciences, 2. See also Cognitive plausibility
• Cold War, 49, 60
• Colmerauer, Alain, 84
• Combinatorial explosion, 182
• Communication network, 249. See also Social network
• Compendium of translation software, 229
• Complexity (linguistic), 18, 23, 182, 195, 255
• Compound words, 15, 23, 33, 46, 164–165, 214, 261
• Comprehension evaluation. See Evaluation measure and test
• Computational linguistics, 15, 36, 37, 68, 82–84
• Computation time, 54, 149, 155, 170,
• Computer documentation, 119
• Confidential data 230–231. See also Intelligence services
• Connected objects. See Smart glasses; Smart watch
• Construction (linguistic), 23
• Context (linguistic), 17–21, 31, 34, 54–56, 64–67, 71, 92, 117–119, 129, 150, 176–178, 186, 188, 215–216, 238
• Continuous model, 186–187
• Conversational agent, 2. See also Artificial dialogue
• Coordination, 175
• Corpus alignment, 91–108
• Cross-language information retrieval, 238–239
• Cryptography, 49, 52, 56, 58–60
• Cryptology. See Cryptography
• CSLi, 232, 236
• Cultural hegemony, 168, 250–251
• Czech, 210, 213

• DARPA, 200–203, 209, 259
• Database access, 241
• Date expressions, 115, 152, 160
• Deceptive cognate, 11, 261
• Decoder, 141, 144, 185, 186, 190
• Deep learning, 34–35, 37, 170, 181–195, 228, 234, 247, 253–255
• Deepmind, 182
• Defense industry, 77, 88, 173, 232–233, 235
• De Firmas-Périés, Arman-Charles-Daniel, 41
• De Maimieux, Joseph, 41
• Descartes, René, 40–42
• Determiner, 133, 215
• Dialogue. See Artificial dialogue
• Dictionary definition, 18, 176–177
• Direct comparability. See Evaluation measure and test
• Direct machine translation, 25–27, 33, 62–64, 68, 124, 156, 158–159
• Directorate General for Translation (European institution), 230, 274
• Direct transfer. See Direct machine translation
• Discontinuous morpheme, 155
• Distortion probability, 140
• Dominant languages. See Language, diversity
• Double alignment, 148–150
• Dynamic programming, 107

• Electronic dictionary. See Bilingual dictionary
• Eliza, 4
• Email, 256
• EM algorithm. See Expectation maximization algorithm
• Empty word, 139
• Encoder, 52, 128, 185–186, 190
• Encryption. See Cryptography
• Endangered languages, 173
• English, 17, 28, 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. See also European institutions
• European Association for Machine Translation, 229
• European Commission. See European institutions
• European Distributed Translation Language project, 44
• European institutions, 85–86, 88, 97, 210, 222–223, 225, 233–234, 245
• European parliament debates. See Europarl corpus
• European patent office, 225, 232
• European Union. See European institutions
• Eurotra (EU project), 86
• Evaluation, 11, 75–81, 197–219. See also Evaluation measure and test
• Evaluation campaign, 200–202, 206, 208, 209–211
• Evaluation measure and test
  • adequacy, 202–204
  • BLEU, 205–207, 212–213
  • comprehension evaluation, 200–201, 204
  • direct comparability, 201 (see also comprehension evaluation)
  • evaluation panel, 201–202
  • fluency, 202–203
  • meteor, 206–207
  • NIST, 206–207
• Evaluation panel. See Evaluation measure and test
• Example-based machine translation, 93, 109–119, 152, 158, 160
• Expectation maximization algorithm, 135

• Facebook, 194, 229, 231, 236
• Fertility probability, 139
• Finnish, 17, 97, 166, 212, 213, 214–215
• Firth, John R., 178, 179, 186
• Fluency. See Evaluation measure and test
• Formal grammar, 58, 59, 63. See also Formal semantic representation
• Formal language, 15–16, 65, 84. See also Formal semantic representation
• Formal semantic representation, 28, 31–34, 59, 60, 65, 70, 84–85, 178–179
• Formulaic expression, 10
• France, 41, 45, 67, 84
• Free online software, 34, 88, 194, 228–231
• French, 17, 30, 53, 63, 155, 167–168, 191, 215, 250
• Frozen expression, 15, 33–34, 167–168, 178, 262
• Fujitsu, 44
• Fully automated high-quality translation (FAHQT), 70–71, 78, 262
• Fully automatic, high-quality machine translation (FAHQMT). See Fully automated high-quality translation
• Fundamental equation of machine translation, 126–131

• Gale, William A., 105
• Garvin, Paul, 76
• Generation (text), 32, 165, 186
• Genetically distant languages, 70, 118, 163, 211
• Georgetown University, 61, 68, 74, 81, 82, 88, 233
• German, 155, 157, 159, 164–165, 214, 250
• Germany, 41
• Gestalt, 20
• Glasses. See Smart glasses
• Goodfellow, Ian, 181, 188, 273
• Google, 33, 36, 159, 167–168, 172, 182, 190, 193–194, 225, 226–229, 232, 234, 236, 240, 243, 248–250
• Google Translation. See Machine translation systems
• Governmental administration, 89, 221, 232, 239, 245, 249. See also Defense industry
• GPU, 188
• Greek, 98, 166, 213
• Gross, Maurice, 68

• Hansard corpus, 96, 105
• Harvard, 63, 81, 83
• Hays, David G., 76, 82
• Hearing-impaired person, 239
• Hierarchical learning, 181, 183, 186–187. See also Deep learning
• Highly inflectional languages, 14. See also Morphologically-rich languages
• HTML tag, 99, 106
• Human-assisted translation, 46–48, 71, 82, 203–204, 246
• Human evaluator, 200–204
• Human language complexity. See Complexity
• Human translator. See Professional translator
• Hungarian, 212, 213
• Hutchins, John, 41, 44, 75, 81–82, 84, 229, 267–270
• Hybrid system machine translation, 165, 170, 171–172, 218, 234

• IBM, 36, 61, 68, 74, 126–145, 185, 197, 199, 200, 209, 212, 228, 232
• IBM WebSphere. See Machine translation systems
• Ideographic writing system, 105
• Idiom. See Idiomatic expression
• Idiomatic expression, 10, 11, 15, 23, 28, 30, 33, 115, 125, 178, 217, 219, 262
• Iida, Hitoshi, 117
• Image recognition, 183
• Indirect machine translation, 25–32
• Indo-European languages, 165, 213, 214, 250
• Information retrieval, 45, 92, 238–239
• Informativeness, 201, 206
• Intelligence industry. See Intelligence services
• Intelligence services, 77, 89, 173, 225, 233, 235, 249
• Interception (of communications), 225, 232
• Interlingua, 24, 28–32, 40, 58, 63, 66–68, 85, 262
• Interlingual machine translation. See Interlingua
• Intermediate representation, 25–32, 63
• Internet, 33, 93, 97, 98, 100, 102, 164, 166, 168–169, 172, 197, 227–233, 238, 242–243, 247–250
• link, 98–99
• Interpretation, 20, 201
• Island of confidence, 102, 108, 150
• Isolating language, 215–216
• Israel, 60, 69

• Japan, 44, 67, 86, 87, 109
• Japanese, 11, 88, 117–118, 164–165, 192, 242
• Jibbigo, 236
• JRC-Acquis corpus, 97, 212–213, 223

• Keyword, 92, 99, 238
• Kilgarriff, Adam, 18
• King, Gilbert, 76
• Kircher, Athanasius, 41
• Koehn, Philip, 136, 212–213
• Korean, 88, 235–236

• Language
  • complexity (see Complexity)
  • diversity, 1, 164–170 (see also typology)
  • exposure (see Child language acquisition)
  • family, 30, 106, 138, 172–174
  • independent representation (see Interlingua)
  • learning (see Child language acquisition)
  • model, 127, 140, 142, 144, 153, 185
  • proximity, 163 (see also family)
  • typology, 138, 192 (see also family)
  • universal, 56, 66, 67 (see also Universal language)
• Lavie, Alon, 206
• Learning step (or learning phase). See Training step
• Legislative text, 95, 222. See also Official text
• Lehmann, Winfred P., 76
• Leibniz, Gottfried Wilhelm, 40–42
• Lemmatizer, 115, 262
• Lexical database. See Lexical resource
• Lexical resource, 67, 68, 124, 159–160, 172, 207, 223, 228, 234. See also Bilingual dictionary
• Lexical semantics, 66, 106–108, 124, 179. See also Lexical resource
• Lexical text alignment, 106–108
• Lexicographer, 176, 177
• Light verbs, 15
• Linguistic complexity. See Complexity
• Linguistic knowledge. See Lexical resource
• Linguistic structure, 12–15, 23, 27, 30, 70, 114–117, 122, 140, 152, 156–160, 164–165, 187, 189, 191, 255
• Linguistic tags, 114, 115, 152. See also Part-of-speech tagging
• Literary text, 11, 12, 100, 154, 197–199
• Logical form, 55, 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, 231 (see also Microsoft)
  • Google Translation, 33, 36, 159, 167–168, 172, 190–194, 225–229, 232, 234, 240, 248–250 (see also Google)
  • IBM WebSphere, 232
  • Metal, 87
  • Météo (TAUM Météo), 84, 87
  • Microsoft Translation (see Bing Translation)
  • Systranet, 226, 228 (see also 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, 171, 176–179, 182, 186–187, 193, 252
• Mechanical brain, 45–46
• Mechanical Translation (journal), 62
• Mel’čuk, 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, 236, 240–243, 250, 256
• Mobile device, 227, 236, 237, 240. See also Mobile phone
• Mobile Internet, 240, 249. See also Mobile phone
• Mobile phone, 232, 236, 240–243, 250, 256
• Model of communication, 52, 55–56, 144
• Morphologically-rich languages, 165, 211–218, 263
• Morphology, 14, 51, 165, 214–216, 263. See also Morphologically-rich languages
• Morse code, 52
• Moses, 212, 213, 223, 259–260
• Mother tongue, 10, 42
• Multidimensional learning, 186
• Multilingual conversation, 240–241
• Multilingual dialogue. See Multilingual conversation
• Multilingual dictionary 44, 46, 51. See also Bilingual dictionary
• Multilingualism 82, 238. See also Multilingual conversation
• Multilingual terminology extraction, 101, 226
• Multiple translation, 92, 166–170
• Multiword expressions, 23, 64, 65, 132, 148, 168, 177. See also Compound words; Idiomatic expression

• Nagao, Makoto, 109–110, 271
• National Institute of Standards and Technology (NIST), 206. See also Evaluation measure and test
• National Science Foundation, 77
• Natural language processing, 15–16, 21, 36, 50, 52, 56–58, 65, 73, 83, 101, 161, 165, 178, 182, 189, 199, 200, 250
• Necker cube, 19–20, 183
• Neural machine translation. See Deep learning
• Neural networks, 181, 185, 188–190, 193, 194, 261, 263. See also Deep learning
• Neuroimaging, 19
• News, translation of, 4, 13, 200, 209
• n-gram, 205–208, 211
• NIST. See Evaluation measure and test
• Noise, 134
• Nonalphabetical writing system, 105
• Nonsense, 11, 14
• Northern Sami, 172
• NTT Docomo, 242
• Nuclear industry, 243, 250
• NUDE, 66–67
• Number of meanings per word, 21, 65

• Oettinger, Anthony G., 76, 82–83, 266
• Official language, 84, 95–96
• Official text, 84, 95–96, 222
• Online phase (or online step). See Testing phase
• Online product sale, 231–233
• OpenLogos, 88
• Oracle, 232

• Papineni, Kishore, 205, 273
• Parallel corpus, 91–108, 110, 121
• Paraphrase, 11, 187
• Parser, 156–159, 263–264
• Particle (linguistic), 18, 117–118
• Part-of-speech tagging, 115, 263
• Pasigraphy, 41
• Patent, 45, 46, 224–225, 232, 238, 248
• Perception, 20, 183
• Person name, 107, 160. See also Proper noun
• Philosophy, 1–3, 8, 39–42, 60, 178
• Phraseology, 92, 119, 225–226, 244
• Pierce, John R., 76
• Pioneers of machine translation, 35, 39–69
• Pivot language, 28, 63, 165–170
• Polysemy, 64, 178
• Porter, Martin, 51
• Porter stemming algorithm, 51
• Portuguese, 56, 213
• Post-edition, 72, 87, 244–245
• Precursors. See Pioneers of machine translation
• Prefab, 23
• Prefix, 214, 264
• Preprocessing (text), 115
• Primitive. See Semantic primitive
• Probability. See Statistical machine translation
• Processing phase (or processing step). See Testing phase
• Professional translator, 10–13, 22, 47, 72, 78–79, 81, 89, 91–92, 109, 121, 199–204, 222, 224, 226–227, 243–246
• Programming language, 15, 58, 65, 84
• Prolog, 84
• Promt, 228, 229, 231, 232, 234
• Pronoun resolution, 175
• Proper noun, 107, 115, 160, 192
• Propositional semantics, 179
• Proust, Marcel, 199
• Pseudo-root (of a word), 51
• Psychology, 8, 76
• Public administration. See Governmental administration
• Punch cards, 46, 73

• Quality of machine translation. See Evaluation
• Query, 238–239, 241

• Rand Corporation, 63
• Reordering rule, 27, 62, 173
• Richens, Richard H., 51, 66, 270
• Rule-based machine translation, 25–32, 49–74, 109, 170–174, 176, 190, 194, 217
• Russia, 45–46, 232
• Russian, 47, 53, 56, 60–61, 63, 79, 81–82, 85, 167, 210, 233, 250

• Sabatakakis, Dimitris, 233
• Samsung, 236, 249, 250
• Schleyer, Johann Martin, 42
• Search engine, 51, 92
• Segment-based machine translation, 147–155
• Semantic primitive, 47, 66–67
• Semantic resources, 67, 159, 160, 172, 207, 228. See also Bilingual dictionary; Lexical resource
• Semantics, 36, 58, 67, 124, 156, 159–161, 174–179, 206
• Sentence
  • alignment, 101–108, 163
  • representation, 19, 24–32, 63, 115–116, 160, 176–179, 185–187, 189
  • structure, 12, 14, 23, 27, 30, 70, 115–116, 117, 122, 140, 152, 156–160, 164, 183, 187, 189, 191, 255
• Sentence-by-sentence translation, 14
• Shallow semantic analysis, 115. See also Semantics
• Shannon, Claude, 52, 55, 76
• Siemens, 87
• Silence, 157, 158
• Skype, 240, 250
• Slavic languages, 214
• Smart glasses, 242–243, 250
• Smart watch, 242–243
• Smirnov-Trojanskij, Petr Petrovitch, 46–48, 51, 246, 269
• Social network, 221, 229, 248
• Sparck Jones, Karen, 66, 270
• Speech-to-speech application, 241–242. See also Speech translation
• Speech transcription, 126, 226, 239–241
• Speech translation, 22, 126, 227, 236, 239–241, 250
• Statistical machine translation, 121–146
• Stemming, 51
• Stratificational grammar, 65
• Style (of a text), 10–13, 15, 46, 55, 92, 208–209
• Suffix, 18, 264. See also Morphology
• Sumita, Eiichiro, 117, 271
• Surface form, 14–15, 17–18, 27, 154, 165, 207–208, 264
• Syllabic writing system, 105
• Symbolic approach, 59, 170–171
• Symmetric alignment, 104, 150. See also Asymmetric alignment
• Synonym, 41, 160, 177, 187, 207–208
• Syntactic duplicate, 11
• Syntactic information. See Syntax
• Syntactic structure, 23, 115–116, 156–159, 255, 264
• Syntax, 30, 60, 65–67, 83, 124, 153, 156–159, 172, 187, 189, 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
• Testing phase (or testing step)
• Text genre, 55, 95, 222
• Theory of meaning, 179
• Toma, Peter, 88, 233
• Tonality (of a text), 10–11
• Training data, 141, 152, 158, 164, 166, 185, 190, 211, 212–214, 218, 234–235, 255
• Training step (or training phase), 131, 141, 152, 187–188, 190, 218. See also Training data
• Transfer, 27–31. See also Transfer rule
• Transfer rule, 30–31, 49, 63, 70, 83, 110, 115, 118, 156, 170–172, 190–191, 234–235, 247, 265
• Translation
  • aid for human translator
  • by analogy (see Example-based machine translation)
  • cost, 76–78, 116, 222–227
  • direction, 92, 164–165, 210
  • expert (see Professional translator)
  • market (see Machine translation market)
  • memory, 64, 92–93, 231, 243–246, 258
  • model, 127–128, 142, 144, 155, 185, 234, 244
  • need, 76, 226, 238–239
  • practice (see Professional translator)
• Translator. See Professional translator
• Turing, Alan, 2, 257
• Turing test, 2
• Twitter, 229, 231
• Typography, 107
• Typology of errors in machine translation, 217–219

• Understanding, 2, 4, 7–8, 10, 19, 21, 23–24, 32, 36
• United Kingdom, 50, 62
• United States of America, 36, 50, 60, 62, 66, 68–70, 75, 77, 83–85
• Universal language, 39–44, 179. See also Esperanto; Volapuk
• Universal Networking Language (UNL), 44
• Universal primitive. See Semantic primitive
• University of Texas, 76, 82–83, 87
• University of Washington, 63, 64, 81
• Unknown word problem, 51, 189–190, 192–194, 217, 253
• URL, 98–99
• US Air Force, 233
• US Army, 233
• US defense industry. See American defense agencies
• User community, 218
• User feedback, 230, 245
• USSR, 62, 68

• Vagueness, 15, 18, 21, 53, 177
• Vauquois, Bernard, 29, 68, 84–86, 156
• Vauquois triangle, 29, 161, 186
• Vector, 185, 188–189
• Vilar, David, 217, 274
• Voice message, 236, 240
• Voice translation. See Speech translation
• Volapuk, 42

• Watson. See Machine translation systems
• Wayne State University, 82–83
• Weather forecast (translation of), 87, 223, 244. See also Météo
• Weaver, Warren, 50, 52–61, 144, 257–258, 269
• Web, 95, 97–100, 106, 197, 222, 226–232
• Web link, 98–99
• WeChat, 240
• Weizenbaum, Joseph, 4
• Well-formedness (of a sentence), 34, 127–128
• White, John S., 200–203, 274
• Wiener, Norbert, 53–54, 61, 66, 257
• Wilkins, John, 41
• Wittgenstein, Ludwig, 178–179
• Word
  • distribution, 255
  • embeddings, 186
  • meaning (see Lexical semantics)
  • order, 27, 49, 62, 128, 138, 142, 191, 217
  • sense, 18, 21, 176–179, 182, 253, 265 (see also Lexical semantics)
  • sense boundaries, 18, 53, 182 (see also Lexical semantics)
  • usage, 18, 176–179
• Word-for-word translation, 11, 13, 22, 27–30, 46, 51, 54–55, 118, 125, 147–148, 150, 157, 167, 215, 218, 253
• Wordnet, 16–17, 159–160, 207
• Workshop on Machine Translation, 209
• World Intellectual Property Organization (WIPO), 225

• Yahoo, 227, 228

• Zamenhof, Ludwik Lejzer, 42