GrammarTagger is an open-source toolkit for grammatical profiling for language learning. It can analyze text in English and Chinese and show you grammatical items included in the input, along with its estimated difficulty.
GrammarTagger is written in Python (3.7+) and AllenNLP (2.1.0+). If you have conda installed, you can set up the environment as follows:
git clone https://github.com/octanove/grammartagger.git
cd grammartagger
conda create -n grammartagger python=3.7
conda activate grammartagger
pip install -r requirements.txt
Also, download the pretrained models (see below). After these steps, you can run GrammarTagger as follows:
English:
echo 'He loves to learn new languages, and last month he practiced some lessons in Spanish.' | python scripts/predict.py model-en-multi.tar.gz | jq
{
"spans": [
{
"span": [0, 3],
"tokens": ["[CLS]", "he", "loves", "to"],
"label": "194:VP.SV.AFF"
},
{
"span": [2, 2],
"tokens": ["loves"],
"label": "60:TA.PRESENT.does.AFF"
},
{
"span": [2, 4],
"tokens": ["loves", "to", "learn"],
"label": "101:TO.VV_to_do"
},
...
],
"tokens": [
"[CLS]", "he", "loves", "to", "learn", "new", "languages", ",",
"and", "last", "month", "he", "practiced", "some", "lessons", "in", "spanish", ".", "[SEP]"
],
"level_probs": {
"c2": 0.008679441176354885,
"b2": 0.005526999477297068,
"c1": 0.05267713591456413,
"b1": 0.06360447406768799,
"a2": 0.06990284472703934,
"a1": 0.7954732775688171
}
}
Chinese:
$ echo '她住得很远，我想送她回去。' | python scripts/predict.py model-zh-multi.tar.gz | jq
{
"spans": [
{
"span": [2, 5],
"tokens": ["住", "得", "很", "远"],
"label": "2.12.1:V 得 A:(using adverbs)"
},
{
"span": [4, 4]
"tokens": ["很"],
"label": "1.06.2:很:very"
},
{
"span": [8, 8],
"tokens": ["想"],
"label": "1.08.1:想:to want"
}
],
"tokens": ["[CLS]", "她", "住", "得", "很", "远", "，", "我", "想", "送", "她", "回", "去", "。", "[SEP]"],
"level_probs": {
"HSK 6": 9.971807230613194e-06,
"HSK 5": 0.0011904890416190028,
"HSK 3": 0.005279902834445238,
"HSK 4": 0.00014815296162851155,
"HSK 2": 0.9917035102844238,
"HSK 1": 0.0016456041485071182
}
}
GrammarTagger is based on pretrained contextualizers, namely BERT (Devlin et al. 2019), and span classification. See the following paper for more details.
- English: model-en-multi.tar.gz (387 MB)
- Chinese: model-zh-multi.tar.gz (363 MB)
These pretrained models are licensed under CC BY-NC-ND 4.0 for academic/personal uses. If you are interested in a commercial license, please contact [email protected]. We are also working on improved models with wider grammar coverage and higher accuracy.
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