Requirements:

git clone https://github.com/CAMeL-Lab/arabic-gec.gitcd arabic-gecconda create -n gec python=3.9conda activate gecpip install -r requirements.txt

cd aretaconda create -n areta python=3.7pip install -r requirements.txt

Experiments and Reproducibility:

Hugging Face Integration:

GED:

fromtransformersimportpipelineged=pipeline('token-classification',model='CAMeL-Lab/camelbert-msa-qalb14-ged-13')text='و قال له انه يحب اكل الطعام بكثره'predictions=ged(text)print(predictions)"""[{'entity': 'MERGE-B', 'score': 0.99943775, 'index': 1, 'word': 'و', 'start': 0, 'end': 1}, {'entity': 'MERGE-I', 'score': 0.99959165, 'index': 2, 'word': 'قال', 'start': 2, 'end': 5}, {'entity': 'UC', 'score': 0.9985884, 'index': 3, 'word': 'له', 'start': 6, 'end': 8}, {'entity': 'REPLACE_O', 'score': 0.8346316, 'index': 4, 'word': 'انه', 'start': 9, 'end': 12}, {'entity': 'UC', 'score': 0.99985325, 'index': 5, 'word': 'يحب', 'start': 13, 'end': 16}, {'entity': 'REPLACE_O', 'score': 0.6836415, 'index': 6, 'word': 'اكل', 'start': 17, 'end': 20}, {'entity': 'UC', 'score': 0.99763715, 'index': 7, 'word': 'الطعام', 'start': 21, 'end': 27}, {'entity': 'REPLACE_O', 'score': 0.993848, 'index': 8, 'word': 'بكثره', 'start': 28, 'end': 33}]"""

GEC:

fromtransformersimportAutoTokenizer,BertForTokenClassification,MBartForConditionalGenerationfromcamel_tools.disambig.bertimportBERTUnfactoredDisambiguatorfromcamel_tools.utils.dediacimportdediac_arimporttorch.nn.functionalasFimporttorchbert_disambig=BERTUnfactoredDisambiguator.pretrained()ged_tokenizer=AutoTokenizer.from_pretrained('CAMeL-Lab/camelbert-msa-qalb14-ged-13')ged_model=BertForTokenClassification.from_pretrained('CAMeL-Lab/camelbert-msa-qalb14-ged-13')gec_tokenizer=AutoTokenizer.from_pretrained('CAMeL-Lab/arabart-qalb14-gec-ged-13')gec_model=MBartForConditionalGeneration.from_pretrained('CAMeL-Lab/arabart-qalb14-gec-ged-13')text='و قال له انه يحب اكل الطعام بكثره .'# morph processing the input texttext_disambig=bert_disambig.disambiguate(text.split())morph_pp_text= [dediac_ar(w_disambig.analyses[0].analysis['diac'])forw_disambigintext_disambig]morph_pp_text=' '.join(morph_pp_text)# GED tagginginputs=ged_tokenizer([morph_pp_text],return_tensors='pt')logits=ged_model(**inputs).logitspreds=F.softmax(logits,dim=-1).squeeze()[1:-1]pred_ged_labels= [ged_model.config.id2label[p.item()]forpintorch.argmax(preds,-1)]# Extending GED label to GEC-tokenized inputged_label2ids=gec_model.config.ged_label2idtokens,ged_labels= [], []forword,labelinzip(morph_pp_text.split(),pred_ged_labels):word_tokens=gec_tokenizer.tokenize(word)iflen(word_tokens)>0:tokens.extend(word_tokens)ged_labels.extend([labelfor_inrange(len(word_tokens))])input_ids=gec_tokenizer.convert_tokens_to_ids(tokens)input_ids= [gec_tokenizer.bos_token_id]+input_ids+ [gec_tokenizer.eos_token_id]label_ids= [ged_label2ids.get(label,ged_label2ids['<pad>'])forlabelinged_labels]label_ids= [ged_label2ids['UC']]+label_ids+ [ged_label2ids['UC']]attention_mask= [1for_inrange(len(input_ids))]gen_kwargs= {'num_beams':5,'max_length':100,'num_return_sequences':1,'no_repeat_ngram_size':0,'early_stopping':False,'ged_tags':torch.tensor([label_ids]),'attention_mask':torch.tensor([attention_mask])              }# GEC generationgenerated=gec_model.generate(torch.tensor([input_ids]),**gen_kwargs)generated_text=gec_tokenizer.batch_decode(generated,skip_special_tokens=True,clean_up_tokenization_spaces=False                                            )[0]print(generated_text)""" وقال له أنه يحب أكل الطعام بكثرة ."""

License:

Citation:

@inproceedings{alhafni-etal-2023-advancements,title ="Advancements in {A}rabic Grammatical Error Detection and Correction: An Empirical Investigation",author ="Alhafni, Bashar  and      Inoue, Go  and      Khairallah, Christian  and      Habash, Nizar",editor ="Bouamor, Houda  and      Pino, Juan  and      Bali, Kalika",booktitle ="Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",month = dec,year ="2023",address ="Singapore",publisher ="Association for Computational Linguistics",url ="https://aclanthology.org/2023.emnlp-main.396",pages ="6430--6448",abstract ="Grammatical error correction (GEC) is a well-explored problem in English with many existing models and datasets. However, research on GEC in morphologically rich languages has been limited due to challenges such as data scarcity and language complexity. In this paper, we present the first results on Arabic GEC using two newly developed Transformer-based pretrained sequence-to-sequence models. We also define the task of multi-class Arabic grammatical error detection (GED) and present the first results on multi-class Arabic GED. We show that using GED information as auxiliary input in GEC models improves GEC performance across three datasets spanning different genres. Moreover, we also investigate the use of contextual morphological preprocessing in aiding GEC systems. Our models achieve SOTA results on two Arabic GEC shared task datasets and establish a strong benchmark on a recently created dataset. We make our code, data, and pretrained models publicly available.",}