Parallel corpora Sample Clauses

Parallel corpora. Req-SMT-001: Parallel corpora must be of sufficient translation quality SMT is able to work properly with parallel corpora, whereas comparable corpora are not adequate. Req-SMT-002: Parallel corpora must be of sufficient size Only sufficient amounts of parallel data can result into a good SMT system. Req-SMT-003: Parallel corpora must be accurately tokenised The tokens in the corpora must be identified and separated by blank spaces. 19 Domain codes are not too helpful. Some can be assigned on monolingual level, some on transfer level; however even in specific domains, the general-purpose readings of a term can occur, and vice versa. 20 cf. Thurmair 2006 Req-SMT-004: Parallel corpora must be aligned at sentence level All sentences in the source language of the parallel corpora must be aligned to their counterpart translations in the target language (and vice versa). Req-SMT-005: Parallel corpora must be in appropriate format The parallel corpus must consist of two files (one for the source language and one for the target language) each containing the same number of lines. Each line must contain one (or more) tokenized sentences so that the ith line in one file is aligned to the ith line in the other one.
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Parallel corpora. ‌ Machine translation systems generally use two separate parallel corpora: at training time, a large corpus is used for extracting translation rules and collecting statistics (usually rule counts), and then at test time, the system is evaluated on a smaller held-out corpus. Systems that need to set parameters (including the one used in our experiments) also require an additional held-out tuning corpus, typically of about the same size as the test set. Our MT training corpus was a 22 million word mixed genre (though primarily newswire) corpus that had been previously assembled and processed to enforce a con- sistent tokenization scheme. The tuning and test sets were taken from the NIST MT04 and MT05 development sets, with some light processing to match the tokenization of the train- ing data. All these datasets were received from BBN as part of the DARPA XXXX (now BOLT) program. Details are in Table 2.2. Corpus Articles Sentences English Words Chinese Words Training 1-270 2261 69k 50k Development 301-325 223 5.0k 3.9k Test 271-300 265 7.6k 5.6k Table 2.3: Bilingual treebank corpus descriptions.

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