58 lines
1.8 KiB
Markdown
58 lines
1.8 KiB
Markdown
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Guess a word in a gap
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=======================================
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Give a probability distribution for a word in a gap in a corpus of
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book "Lalka". This is a challenge for
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language models.
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The metric is log-loss calculated on 10-bit fingerprints generated
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with MurmurHash3 hash function.
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Example
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-------
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For instance, you are expected guess the word in the
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gap marked with <MASK> token here:
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> Wokulski słuchał go uważnie do Pruszkowa .
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> Za Pruszkowem zmęczony i jednostajny głos pana
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> Tomasza zaczął go męczyć . Za to coraz
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> wyraźniej wpadała mu w ucho rozmowa panny
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> Izabeli ze <MASK> , prowadzona po angielsku .
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> Usłyszał nawet kilka zdań , które go zainteresowały ,
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> i zadał sobie pytanie : czy nie należałoby ostrzec
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> ich , że on rozumie po angielsku ?
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(And the correct expected word here is *rolnej*.)
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Directory structure
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-------------------
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* `README.md` — this file
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* `config.txt` — GEval configuration file
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* `train/` — directory with training data
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* `dev-0/` — directory with dev (test) data
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* `dev-0/in.tsv` — input data for the dev set
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* `dev-0/expected.tsv` — expected (reference) data for the dev set
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* `test-A` — directory with test data
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* `test-A/in.tsv` — input data for the test set
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* `test-A/expected.tsv` — expected (reference) data for the test set (hidden)
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Format of the output files
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--------------------------
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For each input line, a probability distribution for words in a gap
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must be given:
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word1:logprob1 word2:logprob2 ... wordN:logprobN :logprob0
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where *logprobi* is the logarithm of the probability for *wordi* and
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*logprob0* is the logarithm of the probability mass for all the other
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words (it will be spread between all 1024 fingerprint values). If the
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respective probabilities do not sum up to 1, they will be normalised with
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softmax.
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