1. A computerized system for performing statistical machine translation, the system comprising:
a statistical machine translation engine executed on a user computing device, the statistical machine translation engine trained on a bilingual parallel corpus including source language documents and a corresponding target human translation of the source language documents, and configured to receive a translation input and to produce a raw machine translation output, at run-time;
a phrasal decoder, separate and distinct from the statistical machine translation engine, executed on the user computing device, the phrasal decoder being trained prior to run-time on a monolingual parallel corpus, the monolingual parallel corpus including a machine translation output of the source language documents of the bilingual parallel corpus and the corresponding target human translation output of the source language documents of the bilingual parallel corpus, to thereby learn mappings and build a phrase table by establishing phrase pairs between the machine translation output and the target human translation output, wherein the machine translation output is unedited by human translators, and wherein the phrasal decoder is trained prior to run-time on a developer computing device on which the bilingual parallel corpus is stored, assigning to each phrase pair a statistical score representing a utility of each phrase pair; and
wherein at run-time on the user computing device the phrasal decoder is configured to process the raw machine translation output, and to produce a corrected translation output based on the learned mappings and the phrase table, programmatically correcting the raw machine translation output if a statistical score for correspondence of the phrase pair is above a predetermined threshold.
2. The computerized system of claim 1, wherein the phrasal decoder is configured to determine the learned mappings by applying a word alignment algorithm.
3. The computerized system of claim 2, wherein the word alignment algorithm is selected from the group consisting of a hidden markov model (HMM), an expectation-maximization (EM) model, a discriminative model, and a syntax-based model.
4. The computerized system of claim 1, wherein the bilingual parallel corpus includes bi-text training data for one or more language pairs, the bi-text training data including source language documents and target human translation output for each language pair.
5. The computerized system of claim 1, wherein the statistical machine translation engine is configured to translate between each of a plurality of language pairs, each language pair having a source language and a target language;
wherein the monolingual parallel corpus is one of a plurality of monolingual parallel corpora that the phrasal decoder is trained on, each of the monolingual parallel corpora being for a target language in one of the language pairs, and each of the monolingual parallel corpora including a machine translation output and a corresponding target human translation output; and
wherein the target human translation output for each monolingual parallel corpora is from a corresponding bilingual parallel corpus for one of the language pairs.
6. The computerized system of claim 5, wherein the language pairs are typologically different language pairs.
7. The computerized system of claim 1, wherein the statistical machine translation engine includes a syntax-based statistical machine translation engine; and
wherein the phrasal decoder includes a phrase-based statistical machine translation engine.
8. The computerized system of claim 1, wherein the phrasal decoder is configured to produce the corrected translation output without prompting a user for a correction input.
9. A computerized method of statistical machine translation, the method comprising:
training a statistical machine translation engine on a bilingual parallel corpus including source language documents and a corresponding target human translation of the source language documents;
training a phrasal decoder, separate and distinct from the statistical machine translation engine, on a monolingual parallel corpus, the monolingual parallel corpus including a machine translation output of the source language documents of the bilingual parallel corpus and the corresponding target human translation output of the source language documents of the bilingual parallel corpus, to thereby learn mappings and build a phrase table by establishing phrase pairs between the machine translation output and the target human translation output, wherein the machine translation output is unedited by human translators, assigning to each phrase pair a statistical score representing a utility of each phrase pair;
performing statistical machine translation via the statistical machine translation engine trained on the bilingual parallel corpus of a translation input to thereby produce a raw machine translation output; and
processing the raw machine translation output to thereby produce a corrected translation output based on the learned mappings and the phrase table, programmatically correcting the raw machine translation output if a statistical score for correspondence of the phrase pair is above a predetermined threshold.
10. The computerized method of claim 9, wherein learning mappings between the machine translation output and the target human translation output includes applying a word alignment algorithm.
11. The computerized method of claim 10, wherein the word alignment algorithm is selected from the group consisting of a hidden markov model (HMM), an expectation-maximization (EM) model, a discriminative model, and a syntax-based model.
12. The computerized method of claim 9, wherein the bilingual parallel corpus includes bi-text training data for one or more language pairs, the bi-text training data including source language documents and target human translation output for each language pair.
13. The computerized method of claim 9, wherein performing statistical machine translation of a translation input includes translating between each of a plurality of language pairs, each language pair having a source language and a target language;
wherein the monolingual parallel corpus is one of a plurality of monolingual parallel corpora that the phrasal decoder is trained on, each of the monolingual parallel corpora being for a target language in one of the language pairs, and each of the monolingual parallel corpora including a machine translation output and a corresponding target human translation output; and
wherein the target human translation output for each monolingual parallel corpora is from a corresponding bilingual parallel corpus for one of the language pairs.
14. The computerized method of claim 13, wherein translating between each of the language pairs includes translating between each of a plurality of typologically different language pairs.
15. The computerized method of claim 9, wherein training the phrasal decoder occurs on a developer computing device on which the bilingual parallel corpus is stored;
wherein performing statistical machine translation of a translation input to thereby produce a raw machine translation output and processing the raw machine translation output to thereby produce a corrected translation output occurs on a user computing device.
16. The computerized method of claim 9, wherein performing statistical machine translation of a translation input is accomplished at least in part by a syntax-based statistical machine translation engine; and
wherein processing the raw machine translation output to thereby produce a corrected translation output is accomplished at least in part by a phrase-based statistical machine translation engine.
17. The computerized method of claim 9, wherein processing the raw machine translation output to thereby produce a corrected translation output occurs without prompting a user for a correction input.
18. A computerized system for performing statistical machine translation, the system comprising:
a user computing device configured to execute:
a statistical machine translation engine trained on a bilingual parallel corpus including source language documents and a corresponding target human translation of the source language documents and configured to receive a translation input and to produce a raw machine translation output, at run-time; and
a phrasal decoder, separate and distinct from the statistical machine translation engine, configured at run-time to process the raw machine translation output, and to produce a corrected translation output for display on a display associated with the system;
wherein the phrasal decoder, separate and distinct from the statistical machine translation engine, is trained on a developer computing device prior to run-time based on a monolingual parallel corpus, the monolingual parallel corpus including a machine translation output of the bilingual parallel corpus and the corresponding target human translation output included within the bilingual parallel corpus, the machine translation output being unedited by human translators, to thereby learn mappings and build a phrase table by establishing phrase pairs between the machine translation output and the target human translation output, assigning to each phrase pair a statistical score representing a utility of each phrase pair, and wherein at run time on the user computing device the corrected translation output is produced based on a plurality of the learned mappings and the phrase table, programmatically correcting the raw machine translation output if a statistical score for correspondence of the phrase pair is above a predetermined threshold.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A wind power installation having an apparatus for monitoring the loading on parts of the wind power installation or the entire wind power installation, wherein the apparatus is arranged in the region of the base of the pylon of the wind power installation and has means with which the loadings on the pylon can be measured in the region of the base of the pylon, characterised in that the wind power installation has a control device which processes the data to be measured for detecting the loading on the pylon and which controls the wind power installation in such a way that a reduction in the speed of rotation of the rotor of the wind power installation andor a reduction in the loading on the wind power installation is effected by means of adjustment of the rotor blades when the measured data exceed a given maximum value once, a plurality of times andor over a certain period of time.
2. A wind power installation according to claim 1 characterised in that the apparatus for detecting the loading on the pylon in the region of the base thereof is a sensor based on resistance strain gauges (RSGs).
3. A wind power installation according to one of the preceding claims characterised by a first device (30) for converting the detected measurement values (detected by the sensor (20)) into analog or digital electrical signals which are representative of the measurement values.
4. Apparatus according to claim 3 characterised by a second device (40) for detecting the electrical signals and for comparing the measurement value represented by the signal to at least one predeterminable first limit value and for displaying when the limit value is reached or exceeded; andor for storing andor cumulating the measurement values represented by the electrical signal.
5. Apparatus according to one of the preceding claims characterised by a device for the transmission of signals which represent individual measurement values andor the cumulated measurement values andor a relationship of the cumulated measurement values with a predeterminable second limit value.
6. A method of monitoring a wind power installation in which data for the loading on the wind power installation are detected by means of a measurement value data pickup, the measurement value data are stored andor processed in accordance with a predetermined method, and the instantaneous loading on the overall wind power installation is ascertained from the measurement value data.
7. A method according to claim 6 characterised in that the ascertained instantaneous loadings are cumulated.
8. A method according to claim 6 characterised in that the ascertained instantaneous loadings are correlated with the instantaneously measured wind.
9. A method according to one of claims 6 to 8 characterised in that the measurement value is compared to a predeterminable first limit value and attainment or exceeding of the limit value is displayed.
10. A method according to one of claims 7 to 9 characterised in that the cumulated measurement values are related to a predeterminable second limit value.
11. A method according to claim 10 characterised in that the relationship between the cumulated measurement values and the second limit value is represented.