Awesome

chrF

a tool for calcualting character n-gram F score

By: Maja Popovic maja.popovic.166@gmail.com, June 2017

chrF++ is a tool for automatic evaluation of machine translation output based on character n-gram precision and recall enhanced with word n-grams. The tool calculates the F-score averaged on all character and word n-grams, where the default character n-gram order is 6 and word n-gram order is 2. The arithmetic mean is used for n-gram averaging.

Recent experiments have shown that adding word 1-grams and 2-grams to the standard character 6-grams improves the Pearson correlation with direct human assessments. If you want to use only character n-grams, just set the word n-gram order to 0.

It is written in Python, so you have to install Python 2 or Python 3. The option -h, --help outputs a description of the available command line options.

Required inputs: ++++++++++++++++

translation reference and hypothesis

The required format of all inputs is raw text containing one sentence per line. Tokenisation is not necessary.

In the case of multiple references, all available reference sentences must be separated by *#

Optional inputs:


-nc, --ncorder
  character n-gram order (default value is 6)

-nw, --nworder
  word n-gram order (default value is 2)

-b, --beta
  beta parameter to balance precision and recall (default value is 2)


Default outputs:
++++++++++++++++
- start time
- overall document level F-score
- overal macro-averaged document level F-score (arithmetic average of the sentence level scores)
- end time

Optional outputs:

-s, --sent sentence level scores

Examples for testing:

You can try the tool on the given examples containing distinct languages: English (en), Czech (cs), Russian (ru) and Chinese (zh). For each language, example.ref.land represents a reference and example.hyp.lang represents a hypothesis.

You can try various calls and compare the results:

a simple call:

English:

chrF++.py -R example.ref.en -H example.hyp.en

start_time: 1497437792 c6+w2-F2 54.9482 c6+w2-avgF2 52.1829 end_time: 1497437792

Russian:

chrF++.py -R example.ref.ru -H example.hyp.ru

start_time: 1497437973 c6+w2-F2 42.2905 c6+w2-avgF2 42.6974 end_time: 1497437973

changing default n-gram orders:

a) chrF++.py -R example.ref.en -H example.hyp.en -nc 8 -nw 1

start_time: 1497438072 c8+w1-F2 52.7801 c8+w1-avgF2 49.7979 end_time: 1497438072

b) chrF++.py -R example.ref.en -H example.hyp.en nw 0 (uses only character n-grams -- recommended for Chinese and similar languages)

start_time: 1497438113 c6+w0-F2 58.0911 c6+w0-avgF2 55.1081 end_time: 1497438113

Chinese:

chrF++.py -R example.ref.zh -H example.hyp.zh -nw 0

start_time: 1497438131 c6+w0-F2 32.6986 c6+w0-avgF2 33.5167 end_time: 1497438131

changing beta parameter:

a) chrF++.py -R example.ref.en -H example.hyp.en -b 1 (equal contribution of precision and recall)

start_time: 1497438189 c6+w2-F1 53.9267 c6+w2-avgF1 50.9922 end_time: 1497438189

b) chrF++.py -R example.ref.en -H example.hyp.en -b 0.4 (more weight on precision)

start_time: 1497438211 c6+w2-F0 52.7434 c6+w2-avgF0 50.0280 end_time: 1497438211

sentence level scores:

chrF+.py -R example.ref.en -H example.hyp.en -s

start_time: 1497438336 1::c6+w2-F2 64.0368 2::c6+w2-F2 70.8799 3::c6+w2-F2 21.5461 4::c6+w2-F2 31.9252 5::c6+w2-F2 44.5054 6::c6+w2-F2 45.0953 7::c6+w2-F2 45.6882 8::c6+w2-F2 54.8102 9::c6+w2-F2 81.0330 10::c6+w2-F2 62.3084 c6+w2-F2 54.9482 c6+w2-avgF2 52.1829 end_time: 1497438336