文件列表:

LICENSE.md (232, 2022-12-05)
corpus (0, 2022-12-05)
corpus\ver1.0 (0, 2022-12-05)
corpus\ver1.0\4multihead-siamese (0, 2022-12-05)
corpus\ver1.0\4multihead-siamese\closed-test-qqp.csv (208159, 2022-12-05)
corpus\ver1.0\4multihead-siamese\test.csv (144365, 2022-12-05)
corpus\ver1.0\4multihead-siamese\train.csv (9529402, 2022-12-05)
corpus\ver1.0\csv-qqp (0, 2022-12-05)
corpus\ver1.0\csv-qqp\.txt (206125, 2022-12-05)
corpus\ver1.0\csv-qqp\closed-test-qqp.csv (208163, 2022-12-05)
corpus\ver1.0\csv-qqp\closed-test.csv (206125, 2022-12-05)
corpus\ver1.0\csv-qqp\open-test.final.manual-qqp.csv (144369, 2022-12-05)
corpus\ver1.0\csv-qqp\open-test.final.manual.csv (142470, 2022-12-05)
corpus\ver1.0\csv-qqp\train-qqp.csv (9529407, 2022-12-05)
corpus\ver1.0\csv-qqp\train.csv (8580488, 2022-12-05)
corpus\ver1.0\csv (0, 2022-12-05)
corpus\ver1.0\csv\.txt (206125, 2022-12-05)
corpus\ver1.0\csv\closed-test.csv (206125, 2022-12-05)
corpus\ver1.0\csv\open-test.final.manual.csv (142470, 2022-12-05)
corpus\ver1.0\csv\train.csv (8580488, 2022-12-05)
corpus\ver1.0\script (0, 2022-12-05)
corpus\ver1.0\script\qqp-test-format.pl (638, 2022-12-05)
corpus\ver1.0\script\qqp-train-format.pl (775, 2022-12-05)
corpus\ver1.0\script\rm-200b-200d.sh (189, 2022-12-05)
corpus\ver1.0\script\tsv2csv.sh (386, 2022-12-05)
corpus\ver1.0\script\tsv2csv4train.sh (429, 2022-12-05)
corpus\ver1.0\tsv (0, 2022-12-05)
corpus\ver1.0\tsv\closed-test (202235, 2022-12-05)
corpus\ver1.0\tsv\open-test.final.manual (138579, 2022-12-05)
corpus\ver1.0\tsv\train.txt (8350480, 2022-12-05)
fig (0, 2022-12-05)
fig\accuracy-CNN-Siamese-myParaphrase.png (70272, 2022-12-05)
fig\accuracy-RNN-Siamese-myParaphrase.png (76514, 2022-12-05)
fig\accuracy-Transformer-Siamese-myParaphrase.png (92913, 2022-12-05)
fig\accuracy-loss-of-3-siamese-model.png (99859, 2022-12-05)
fig\evaluation-on-3-siamese-models-accuracy-fig3.png (137766, 2022-12-05)
fig\evaluation-on-3-siamese-models-fig1.png (136923, 2022-12-05)
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# myParaphrase Paraphrase Dataset for Burmese (Myanmar Language) Paraphrase detection or semantic similarity of necessity is to understand the sentence as a whole sentence, but not just finding synonyms of the words. It is an important research area in natural language processing that plays a significant role in many applications such as question answering, summarization, information retrieval, and extraction. To the best of our knowledge, no studies have been conducted on Burmese (Myanmar language) paraphrase or not paraphrase detection and classification. We did paraphrase classification experiments beased on both traditional machine learning methods and deep Siamese neural networks. One more contribution is the development of the human-annotated combination of Burmese paraphrase and non-paraphrase corpus that contained 40,461 sentence pairs and open-test data with 1,000 sentence pairs. ## Introduction in Burmese (Myanmar Language) corpus paraphrase corpus NLP Ph.D. open-test data corpus ## Versions Information Version 1.0 Release Date: 3 December 2022 ## Data Format Example CSV header is as follows: ``` "id","pid1","pid2","paraphrase1","paraphrase2","is_paraphrase" ``` Some examples of tagged paraphrase sentences are as follows: ``` (base) yekyaw.thu@gpu:~/exp/siamese/myParaphrase/corpus/ver1.0/csv-qqp$ shuf train-qqp.csv | head "1***30","1***31","1***32"," "," ","0" "24566","24567","24568"," "," ","0" "28755","28756","28757"," "," ","1" "23088","23089","23090"," "," ","0" "28697","286***","28699"," "," ","0" "14700","14701","14702"," "," ","0" "16027","16028","16029"," "," ","1" "22766","22767","22768"," "," ","0" "26023","26024","26025"," "," ","0" "24002","24003","24004"," "," ","0" ``` ## Contributors Myint Myint Htay Ye Kyaw Thu ## Experimental Setting for Demo Running We used only training data for demo running. For the three Siamese Neural Network model building, we used [https://github.com/tlatkowski/multihead-siamese-nets](https://github.com/tlatkowski/multihead-siamese-nets). Some important hyperparameters for all three models are as follows: ``` [TRAINING] num_epochs = 10 batch_size = 512 eval_every = 20 learning_rate = 0.001 checkpoints_to_keep = 5 save_every = 100 log_device_placement = False [DATA] logs_path = logs model_dir = model_dir [PARAMS] embedding_size = *** loss_function = MSE char_embeddings = False ``` Specific hyperparameter for RNN: ``` [PARAMS] hidden_size = 128 cell_type = GRU bidirectional = True ``` Specific hyperparameter for CNN: ``` [PARAMS] num_filters = 50,50,50 filter_sizes = 2,3,4 dropout_rate = 0.0 ``` Specific hyperparameter for Transformer: ``` [PARAMS] num_blocks = 2 num_heads = 8 use_residual = False dropout_rate = 0.0 ``` ## Experimental Results

Table.1 Evaluation results on RNN-Siamese, CNN-Siamese and Transformer-Siamese with myParaphrase corpus (version 1.0)

## Citation If you want to use myParaphrase corpus (version 1.0) in your research and we'd appreciate if you use the following reference: Myint Myint Htay, Ye Kyaw Thu, Hnin Aye Thant, Thepchai Supnithi, "Deep Siamese Neural Network Vs Random Forest for Myanmar Language Paraphrase Classification", Journal of Intelligent Informatics and Smart Technology, Oct 2nd Issue, 2022, pp. 25-1 to 25-9. (Submitted Feb 21, 2022; accepted July 17, 2022; published on 31 Oct 2022) [[Paper]](https://jiist.aiat.or.th/assets/uploads/1667145660263IBU7L25_Deep%20Siamese%20Neural%20Network%20Vs%20Random%20Forest%20for%20Myanmar%20Language%20Paraphrase%20Classification.pdf) If you want to use three Siamese models that we trained with myParaphrase (version 1.0) and please cite the following link: https://github.com/ye-kyaw-thu/myParaphrase ## Reference We did paraphrase classification experiments with "multihead-siamese-nets": [1] [https://github.com/tlatkowski/multihead-siamese-nets](https://github.com/tlatkowski/multihead-siamese-nets) Some run time errors were solved based on followings: [2] [https://stackoverflow.com/questions/55318626/module-tensorflow-has-no-attribute-logging](https://stackoverflow.com/questions/55318626/module-tensorflow-has-no-attribute-logging) [3] [https://stackoverflow.com/questions/61102281/dataframe-object-has-no-attribute-as-matrix](https://stackoverflow.com/questions/61102281/dataframe-object-has-no-attribute-as-matrix) Read some papers: [4] ``` @inproceedings{NIPS2017_3f5ee243, author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, \L ukasz and Polosukhin, Illia}, booktitle = {Advances in Neural Information Processing Systems}, editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett}, pages = {}, publisher = {Curran Associates, Inc.}, title = {Attention is All you Need}, url = {https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf}, volume = {30}, year = {2017} } ``` [5] ``` @inproceedings{ranasinghe-etal-2019-semantic, title = "Semantic Textual Similarity with {S}iamese Neural Networks", author = "Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan", booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)", month = sep, year = "2019", address = "Varna, Bulgaria", publisher = "INCOMA Ltd.", url = "https://aclanthology.org/R19-1116", doi = "10.26615/978-954-452-056-4_116", pages = "1004--1011", abstract = "Calculating the Semantic Textual Similarity (STS) is an important research area in natural language processing which plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction. This paper evaluates Siamese recurrent architectures, a special type of neural networks, which are used here to measure STS. Several variants of the architecture are compared with existing methods", } ``` [6] ``` @inproceedings{Koch2015SiameseNN, title={Siamese Neural Networks for One-Shot Image Recognition}, author={Gregory R. Koch}, year={2015} } ``` ## To Do - to update the myParaphrase corpus - to study on longer Burmese sentences and paragraph level

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