Feature Decay Algorithms (FDA)
Citation:
Ergun Bicici and Deniz Yuret, “Optimizing Instance Selection for Statistical Machine Translation with Feature Decay Algorithms”, IEEE/ACM Transactions On Audio, Speech, and Language Processing (TASLP), 2014.
FDA is developed as part of my PhD thesis:
Ergun Biçici. The Regression Model of Machine Translation. PhD thesis, Koç University, 2011. Note: Supervisor: Deniz Yuret.
Usage: fda [opts] train1 test1 [train2] [test2]\n" "train1: first (mandatory) arg gives source language train file\n" "test1 : second (mandatory) arg gives source language test file\n" "train2: third (optional) arg gives target language train file\n" " this is used to output target sentences as a second column\n" "test2 : fourth (optional) arg gives target language test file\n" " this is used to calculate metrics like bigram coverage\n" "If any of these arguments are "-", the data is read from stdin.\n" "Gzip compressed files are automatically recognized and handled.\n" "Other options (and defaults) are:\n" "-v (1): verbosity level, -v0 no messages, -v2 more detail\n" "-n (3): maximum ngram order for features\n" "-t (0): number of training words output, -t0 means no limit\n" "-o (null): output file, stdout is used if not specified\n" "The rest of the options are used to calculate feature and sentence scores:\n" "-i (1.0): initial feature score idf exponent\n" "-l (1.0): initial feature score ngram length exponent\n" "-d (0.5): final feature score decay factor\n" "-c (0.0): final feature score decay exponent\n" "-s (1.0): sentence score length exponent\n" "Formulas:\n" "initial feature score: fscore0 = idf^i * ngram^l\n" "final feature score : fscore1 = fscore0 * d^cnt * cnt^(-c)\n" "sentence score : sscore = sum_fscore1 * slen^(-s)\n"