Statistical Machine Translation
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1 Statistical Machine Translation What works and what does not Andreas Maletti Universität Stuttgart Stuttgart May 14, 2013 Statistical Machine Translation A. Maletti 1
2 Main notions Machine translation (MT) Automatic natural language translation as opposed to: manual translation computer-aided translation (by a computer) (e.g., translation memory) Statistical machine translation (SMT) MT using systems automatically obtained from (many) translations as opposed to: rule-based machine translation example-based machine translation (old) SYSTRAN translation by analogy Statistical Machine Translation A. Maletti 2
3 Main notions Machine translation (MT) Automatic natural language translation as opposed to: manual translation computer-aided translation (by a computer) (e.g., translation memory) Statistical machine translation (SMT) MT using systems automatically obtained from (many) translations as opposed to: rule-based machine translation example-based machine translation (old) SYSTRAN translation by analogy Statistical Machine Translation A. Maletti 2
4 Short history Timeline 1 Dark age (60s 90s) rule-based systems (e.g., SYSTRAN) CHOMSKYAN approach perfect translation, poor coverage 2 Reformation (1991 present) phrase-based and syntax-based systems statistical approach cheap, automatically trained 3 Potential future semantics-based systems (e.g., FRAMENET-based) semi-supervised, statistical approach basic understanding of translated text Statistical Machine Translation A. Maletti 3
5 Short history Timeline 1 Dark age (60s 90s) rule-based systems (e.g., SYSTRAN) CHOMSKYAN approach perfect translation, poor coverage 2 Reformation (1991 present) phrase-based and syntax-based systems statistical approach cheap, automatically trained 3 Potential future semantics-based systems (e.g., FRAMENET-based) semi-supervised, statistical approach basic understanding of translated text Statistical Machine Translation A. Maletti 3
6 Short history Timeline 1 Dark age (60s 90s) rule-based systems (e.g., SYSTRAN) CHOMSKYAN approach perfect translation, poor coverage 2 Reformation (1991 present) phrase-based and syntax-based systems statistical approach cheap, automatically trained 3 Potential future semantics-based systems (e.g., FRAMENET-based) semi-supervised, statistical approach basic understanding of translated text Statistical Machine Translation A. Maletti 3
7 Standard pipeline Schema Input Translation model Language model Output (the models are often integrated in practice) Required resources bilingual text (sentences in both languages) monolingual text (in target language) 1.5M sent. 44M sent. Statistical Machine Translation A. Maletti 4
8 Standard pipeline Schema Input Translation model Language model Output (the models are often integrated in practice) Required resources bilingual text (sentences in both languages) monolingual text (in target language) 1.5M sent. 44M sent. Statistical Machine Translation A. Maletti 4
9 Standard pipeline Schema Input Translation model Language model Output (the models are often integrated in practice) Required resources bilingual text (sentences in both languages) monolingual text (in target language) 1.5M sent. 44M sent. Statistical Machine Translation A. Maletti 4
10 Standard pipeline Example (Source: GOOGLE translate) Input: What works and what does not Segmentation: What works and what does not Translation model output: Was funktioniert Was am Was funktioniert am und was nicht und was nicht funktioniert und welche nicht ist und was nicht Statistical Machine Translation A. Maletti 5
11 Standard pipeline Example (Source: GOOGLE translate) Input: What works and what does not Segmentation: What works and what does not Translation model output: Was funktioniert Was am Was funktioniert am und was nicht und was nicht funktioniert und welche nicht ist und was nicht Statistical Machine Translation A. Maletti 5
12 Standard pipeline Example (Source: GOOGLE translate) Input: What works and what does not Segmentation: What works and what does not Translation model output: Was funktioniert Was am Was funktioniert am und was nicht und was nicht funktioniert und welche nicht ist und was nicht Statistical Machine Translation A. Maletti 5
13 Standard pipeline Example (Source: GOOGLE translate) Input: What works and what does not Segmentation: What works and what does not Translation model output: Was funktioniert Was am Was funktioniert am und was nicht und was nicht funktioniert und welche nicht ist und was nicht Statistical Machine Translation A. Maletti 5
14 Phrase-based machine translation And then the matter was decided, and everything was put in place f kan An tm AlHsm w wdet Almwr fy nsab ha Extracted information Segmentation: And then the matter was decided, and everything was put in place Phrase translation: Reordering: Statistical Machine Translation A. Maletti 6
15 Phrase-based machine translation And then the matter was decided, and everything was put in place f kan An tm AlHsm w wdet Almwr fy nsab ha Extracted information Segmentation: And then the matter was decided, and everything was put in place Phrase translation: Reordering: Statistical Machine Translation A. Maletti 6
16 Phrase-based machine translation And then the matter was decided, and everything was put in place f kan An tm AlHsm w wdet Almwr fy nsab ha Extracted information Segmentation: And then 1 the matter 2 was decided 3, and everything 4 was put 5 in place 6 Phrase translation: Reordering: Statistical Machine Translation A. Maletti 6
17 Phrase-based machine translation And then the matter was decided, and everything was put in place f kan An tm AlHsm w wdet Almwr fy nsab ha Extracted information Segmentation: And then 1 the matter 2 was decided 3, and everything 4 was put 5 in place 6 Phrase translation: f kan 1 Almwr 2 An tm AlHsm 3 w 4 wdet 5 fy nsab ha 6 Reordering: Statistical Machine Translation A. Maletti 6
18 Phrase-based machine translation And then the matter was decided, and everything was put in place f kan An tm AlHsm w wdet Almwr fy nsab ha Extracted information Segmentation: And then 1 the matter 2 was decided 3, and everything 4 was put 5 in place 6 Phrase translation: f kan 1 Almwr 2 An tm AlHsm 3 w 4 wdet 5 fy nsab ha 6 Reordering: ( ) Statistical Machine Translation A. Maletti 6
19 How it works Technical talks Marion Weller Daniel Quernheim and Nina Seemann phrase-based MT syntax-based MT Statistical Machine Translation A. Maletti 7
20 Small players Research at IMS Phrase-based MT (head: Dr. Alexander Fraser) Fabienne Braune Fabienne Cap Anita Ramm Marion Weller Syntax-based MT (head: Dr. Andreas Maletti) Fabienne Braune Daniel Quernheim Nina Seemann Statistical Machine Translation A. Maletti 8
21 Small players Research at IMS Phrase-based MT (head: Dr. Alexander Fraser) Fabienne Braune Fabienne Cap Anita Ramm Marion Weller Syntax-based MT (head: Dr. Andreas Maletti) Fabienne Braune Daniel Quernheim Nina Seemann Statistical Machine Translation A. Maletti 8
22 Small players Research at IMS Phrase-based MT (head: Dr. Alexander Fraser) Fabienne Braune Fabienne Cap Anita Ramm Marion Weller Syntax-based MT (head: Dr. Andreas Maletti) Fabienne Braune Daniel Quernheim Nina Seemann Statistical Machine Translation A. Maletti 8
23 Big players Commercial systems Language Studio GOOGLE translate WebSphere Translation Server BING translator OMNIFLUENT... Statistical Machine Translation A. Maletti 9
24 Big players Commercial systems Language Studio GOOGLE translate WebSphere Translation Server BING translator OMNIFLUENT... Soon also Statistical Machine Translation A. Maletti 9
25 Failures Statistical Machine Translation A. Maletti 10
26 Failures Applications Technical manuals Example (An mp3 player) The synchronous manifestation of lyrics is a procedure for can broadcasting the music, waiting the mp3 file at the same time showing the lyrics. With the this kind method that the equipments that synchronous function of support up broadcast to make use of document create setup, you can pass the LCD window way the check at the document contents that broadcast. That procedure returns offerings to have to modify, and delete, and stick top, keep etc. edit function. Statistical Machine Translation A. Maletti 11
27 Failures Applications Technical manuals Example (An mp3 player) The synchronous manifestation of lyrics is a procedure for can broadcasting the music, waiting the mp3 file at the same time showing the lyrics. With the this kind method that the equipments that synchronous function of support up broadcast to make use of document create setup, you can pass the LCD window way the check at the document contents that broadcast. That procedure returns offerings to have to modify, and delete, and stick top, keep etc. edit function. Statistical Machine Translation A. Maletti 11
28 Failures Applications Technical manuals Example (An mp3 player) The synchronous manifestation of lyrics is a procedure for can broadcasting the music, waiting the mp3 file at the same time showing the lyrics. With the this kind method that the equipments that synchronous function of support up broadcast to make use of document create setup, you can pass the LCD window way the check at the document contents that broadcast. That procedure returns offerings to have to modify, and delete, and stick top, keep etc. edit function. Statistical Machine Translation A. Maletti 11
29 Failures Applications Technical manuals Example (Hotel Uppsala, Sweden) Wir hatten die Zimmer eingestuft wird als Superior weil sie renoviert wurde im letzten Jahr oder zwei. Unsere Zimmer hatten Parkettboden und waren sehr geräumig. Man musste allerdings nicht musste seitwärts bewegen. Statistical Machine Translation A. Maletti 11
30 Failures Applications Technical manuals Example (Hotel Uppsala, Sweden) Wir hatten die Zimmer eingestuft wird als Superior weil sie renoviert wurde im letzten Jahr oder zwei. Unsere Zimmer hatten Parkettboden und waren sehr geräumig. Man musste allerdings nicht musste seitwärts bewegen. We stayed in rooms classified as superior because they had been renovated in the last year or two. Our rooms had wood floors and were roomy. You didn t have to walk sideways to move around. Statistical Machine Translation A. Maletti 11
31 Failures Applications Technical manuals US military Example (JONES, SHEN, HERZOG 2009) Soldier: Okay, what is your name? Local: Abdul. Soldier: And your last name? Local: Al Farran. Statistical Machine Translation A. Maletti 11
32 Failures Applications Technical manuals US military Example (JONES, SHEN, HERZOG 2009) Soldier: Okay, what is your name? Local: Abdul. Soldier: And your last name? Local: Al Farran. Speech-to-text machine translation Soldier: Local: Okay, what s your name? milk a mechanic and I am here I mean yes Statistical Machine Translation A. Maletti 11
33 Failures Applications Technical manuals US military Example (JONES, SHEN, HERZOG 2009) Soldier: Okay, what is your name? Local: Abdul. Soldier: And your last name? Local: Al Farran. Speech-to-text machine translation Soldier: Local: Soldier: Local: Okay, what s your name? milk a mechanic and I am here I mean yes What is your last name? every two weeks my son s name is ismail Statistical Machine Translation A. Maletti 11
34 Failures Applications Technical manuals US military MSDN, Knowledge Base... Statistical Machine Translation A. Maletti 11
35 But in many cases it actually works... Statistical Machine Translation A. Maletti 12
36 Selected application Lecture translation real-time speech-to-text machine translation combines automatic speech recognition and SMT requires lecturer training and terminology training automatically provides subtitles to lecture video Video Statistical Machine Translation A. Maletti 13
37 Selected application Lecture translation real-time speech-to-text machine translation combines automatic speech recognition and SMT requires lecturer training and terminology training automatically provides subtitles to lecture video Video Statistical Machine Translation A. Maletti 13
38 Summary SMT works well between similar languages (e.g., Spanish-English) between large resource languages (e.g., French-English) in-domain (training and test from the same domain) access to foreign language SMT could be better into morphologically rich / free word order languages (e.g., German) handling noisy inputs (e.g., chats, Twitter feeds) dealing with documents (instead of sentences) precision / translation accuracy Conclusion SMT is a cheap way to access foreign material Statistical Machine Translation A. Maletti 14
39 Summary SMT works well between similar languages (e.g., Spanish-English) between large resource languages (e.g., French-English) in-domain (training and test from the same domain) access to foreign language SMT could be better into morphologically rich / free word order languages (e.g., German) handling noisy inputs (e.g., chats, Twitter feeds) dealing with documents (instead of sentences) precision / translation accuracy Conclusion SMT is a cheap way to access foreign material Statistical Machine Translation A. Maletti 14
40 Summary SMT works well between similar languages (e.g., Spanish-English) between large resource languages (e.g., French-English) in-domain (training and test from the same domain) access to foreign language SMT could be better into morphologically rich / free word order languages (e.g., German) handling noisy inputs (e.g., chats, Twitter feeds) dealing with documents (instead of sentences) precision / translation accuracy Conclusion SMT is a cheap way to access foreign material Statistical Machine Translation A. Maletti 14
41 Summary SMT works well between similar languages (e.g., Spanish-English) between large resource languages (e.g., French-English) in-domain (training and test from the same domain) access to foreign language SMT could be better into morphologically rich / free word order languages (e.g., German) handling noisy inputs (e.g., chats, Twitter feeds) dealing with documents (instead of sentences) precision / translation accuracy Conclusion SMT is a cheap way to access foreign material Statistical Machine Translation A. Maletti 14
42 Summary SMT works well between similar languages (e.g., Spanish-English) between large resource languages (e.g., French-English) in-domain (training and test from the same domain) access to foreign language SMT could be better into morphologically rich / free word order languages (e.g., German) handling noisy inputs (e.g., chats, Twitter feeds) dealing with documents (instead of sentences) precision / translation accuracy Conclusion SMT is a cheap way to access foreign material Statistical Machine Translation A. Maletti 14
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