Changes between Initial Version and Version 1 of en/AdvancedNlpCourse2019/MachineTranslation


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Timestamp:
Oct 1, 2020, 3:33:17 PM (12 months ago)
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Ales Horak
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copied from private/AdvancedNlpCourse/MachineTranslation

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  • en/AdvancedNlpCourse2019/MachineTranslation

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     1= Machine translation =
     2
     3[[https://is.muni.cz/auth/predmet/fi/ia161|IA161]] [[en/AdvancedNlpCourse|Advanced NLP Course]], Course Guarantee: Aleš Horák
     4
     5Prepared by: Vít Baisa
     6
     7== State of the Art ==
     8
     9The Statistical Machine Translation consists of two main parts: a language model for a target language which is responsible for fluency and good-looking output sentences and a translation model which translates source words and phrases into target language. Both models are probability distributions and can be built using a monolingual corpus for language model and a parallel corpus for translation model.
     10
     11=== References ===
     12
     13Approx 3 current papers (preferably from best NLP conferences/journals, eg. [[https://www.aclweb.org/anthology/|ACL Anthology]]) that will be used as a source for the one-hour lecture:
     14
     15 1. Koehn, Philipp, et al. "Moses: Open source toolkit for statistical machine translation." Proceedings of the 45th annual meeting of the ACL on interactive poster and demonstration sessions. Association for Computational Linguistics, 2007.
     16 1. Koehn, Philipp, Franz Josef Och, and Daniel Marcu. "Statistical phrase-based translation." Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology-Volume 1. Association for Computational Linguistics, 2003.
     17 1. Denkowski, Michael, and Alon Lavie. "Meteor 1.3: Automatic metric for reliable optimization and evaluation of machine translation systems." Proceedings of the Sixth Workshop on Statistical Machine Translation. Association for Computational Linguistics, 2011.
     18
     19== Workshop: generating translation dictionary from parallel data ==
     20
     21=== Basic instructions ===
     22
     23* download [raw-attachment:ia161_mt.tar.gz ia161_mt.tar.gz] with scripts and train data
     24* unzip with {{{tar xzf ia161_mt.tar.gz}}}
     25* subdir {{{ia161_mt}}} will be created
     26
     27=== Files ===
     28
     29||czech.words||100,000 sentences from Czech part of DGT-TM||
     30||czech.lemmas||100,000 sentences (lemmas) from Czech part of DGT||
     31||english.words||100,000 sentences from English DGT||
     32||english.lemmas||100,000 sentences (lemmas) from EN DGT||
     33||eval.py||a script for evaluation of coverage and precision of a generated dictionary using a small English-Czech dictionary||
     34||gnudfl.txt||a small English-Czech dictionary containing only one-word items and words from the training data||
     35||make_dict.py||a script for generating a translation dictionary based on co-occurrences and frequency lists||
     36||Makefile||a file with rules for building the dictionary based on the training data||
     37||par2items.py||a file for generating pairs of words (lemmas) from the parallel data||
     38
     39=== Description of make ===
     40
     41{{{make dict}}}
     42
     43* the command uses 1,000 lines from training data and generates a dictionary based on wordforms (files czech.words and english.words)
     44* it is possible to use alternative files with lemmas using parameter L1DATA and L2DATA
     45* it is also possible to change the number of lines used for the computation (parameter LIMIT)
     46* in general: {{{make dict [L1DATA=<file>] [L2DATA=<file>] [LIMIT=<number of lines>]}}}
     47* e.g.: {{{make dict L1DATA=english.lemmas L2DATA=czech.lemmas LIMIT=10000}}}
     48
     49The 1,000 lines by default are for the sake of speed.
     50
     51{{{make eval}}}
     52
     53* when the dictionary is generated, you can measure its precision and coverage using script eval.py: {{{make eval}}}.
     54* if you use parameters {{{L1DATA}}} and {{{L2DATA}}}, you must repeat them {{{make eval}}}
     55* e.g.: {{{make dict L1DATA=english.lemmas L2DATA=czech.lemmas}}}
     56
     57{{{make clean}}}
     58
     59* after each change to the input files or the scripts or parameters, clean temporary files: {{{make clean}}}
     60
     61== Detailed description of the scripts and generated data ==
     62
     63* Try to run default {{{make dict}}} and look at the results:
     64  * czech.words.freq
     65  * english.words.freq
     66  * english.words-czech.words.cofreq
     67  * english.words-czech.words.dict (the resulting dictionary)
     68* Look at sizes of the output files (how many lines they contain) and its contents.
     69* Look at the script {{{make_dict.py}}}, which generates the dictionary: at key places it contains {{{TODO}}}
     70* there you can change the script, add heuristics, change conditions etc. so the final f-score is the highest possible
     71
     72== Assignment ==
     73
     741. Change the key places of scripts {{{par2items.py}}}, {{{make_dict.py}}} to achieve the highest possible f-score (see {{{make eval}}}).
     751. Upload all the scripts into the vault in one archive file.
     761. You can create it like this: {{{tar czf ia161_mt_<uco_or_login>.tar.gz Makefile *.py}}}