DMT

VarDial19 shared task: discriminating between mainland and taiwan variation of mandarin chinese -- DMT

Dataset

Download dmt data here, put it in raw_data dir.

Data augmentation

Cut long sentences into multiple shorter sub-sentences and replace the original sentence in the training data.

DL based Models

TextCNN, EMNLP2014
Kim et al. Convolutional Neural Networks for Sentence Classification.
DCNN, ACL2014
Kalchbrenner et al. A Convolutional Neural Network for Modelling Sentences
RCNN, AAAI2015
Lai et al. Recurrent Convolutional Neural Networks for Text Classification.
HAN, NAACL-HLT2016
Yang et al. Hierarchical Attention Networks for Document Classification.
DPCNN, ACL2017
Johnson et al. Deep Pyramid Convolutional Neural Networks for Text Categorization.
VDCNN, EACL2017
Conneau et al. Very Deep Convolutional Networks for Text Classification.
MultiTextCNN
Extension of textcnn, stacking multiple cnns with the same filter size.
BiLSTM
Bidirectional lstm + max pooling over time.
RNNCNN
Bidirectional gru + conv + max pooling & avg pooling.
CNNRNN
Conv + max pooling + Bidirectional gru + max pooling over time.

ML based model

features
binary ngram(1-3), tf ngram(1-3), tfidf ngram(1-3), skip ngram (1-3 skip bigram or trigram)in character & word level, pos ngram
models
lr, svm, navie-bayers, random-forest, gradientboost, xgboost

Dialect matching model

Learn similarities between the same dialects and dissimilarities between different dialects.

Ensemble-based model

mean ensemble
max ensemble
majortiy-vote ensemble
lda
xgboost
rf
lightgbm

Pre-processing

python3 preprocess.py

Train

python3 train.py

Ensemble

python3 ensemble_models.py

Performance of dl based models

Simplified

model	val_acc	val_f1	train_time(one titan x)
simplified_bilstm_word_w2v_data_tune	0.9	0.8996	00:08:26
simplified_bilstm_word_w2v_data_tune_0.48	0.9015	0.9016	-
simplified_bilstm_word_w2v_data_tune_0.46	0.902	0.9025	-
simplified_aug_bilstm_word_w2v_data_tune	0.8965	0.8965	00:10:00
simlifiedd_cnnrnn_word_w2v_data_tune	0.8935	0.8923	00:09:07
simplified_dcnn_word_w2v_data_tune	0.897	0.8971	00:02:08
simplified_dpcnn_word_w2v_data_tune	0.8925	0.8932	00:00:39
simplified_han_word_w2v_data_tune	0.8915	0.8896	00:06:50
simplified_multicnn_word_w2v_data_tune	0.5	0.0	00:01:13
simplified_rcnn_word_w2v_data_tune	0.8985	0.8965	00:08:27
simplified_rnncnn_word_w2v_data_tune	0.895	0.8960	00:06:09
simplified_cnn_word_w2v_data_tune	0.8965	0.8998	00:00:43
simplified_vdcnn_word_w2v_data_tune	0.871	0.8716	00:18:03
simplified_bilstm_word_w2v_data_fix	0.8445	0.8449	00:16:23
simplified_dcnn_word_w2v_data_fix	0.816	0.8122	00:03:46
simplified_dpcnn_word_w2v_data_fix	0.8245	0.8152	00:00:57
simplified_han_word_w2v_data_fix	0.8345	0.8346	00:15:44
simplified_multicnn_word_w2v_data_fix	0.5	0.6667	00:01:10
simplified_rcnn_word_w2v_data_fix	0.844	0.8421	00:12:03
simplified_rnncnn_word_w2v_data_fix	0.8335	0.8316	00:09:07
simplified_cnn_word_w2v_data_fix	0.825	0.8227	00:00:46
simplified_vdcnn_word_w2v_data_fix	0.7935	0.7800	00:11:59
simplified_bilstm_char_w2v_data_tune	0.849	0.8442	00:26:15
simplified_dcnn_char_w2v_data_tune	0.8525	0.8509	00:04:22
simplified_dpcnn_char_w2v_data_tune	0.8605	0.8600	00:01:06
simplified_han_char_w2v_data_tune	0.5	0.0	00:11:57
simplified_multicnn_char_w2v_data_tune	0.5	0.0	00:02:17
simplified_rcnn_char_w2v_data_tune	0.85	0.8416	00:20:21
simplified_rnncnn_char_w2v_data_tune	0.8585	0.8573	00:16:44
simplified_cnn_char_w2v_data_tune	0.874	0.8746	00:02:41
simplified_vdcnn_char_w2v_data_tune	0.5095	0.1264	00:06:58
simplified_bilstm_char_w2v_data_fix	0.809	0.8068	00:35:03
simplified_dcnn_char_w2v_data_fix	0.7565	0.7514	00:04:49
simplified_dpcnn_char_w2v_data_fix	0.7985	0.8000	00:02:36
simplified_han_char_w2v_data_fix	0.576	0.6428	00:11:57
simplified_multicnn_char_w2v_data_fix	0.5	0.6667	00:02:11
simplified_rcnn_char_w2v_data_fix	0.8145	0.8179	00:36:16
simplified_rnncnn_char_w2v_data_fix	0.7945	0.7876	00:19:18
simplified_cnn_char_w2v_data_fix	0.8155	0.8073	00:02:30
simplified_vdcnn_char_w2v_data_fix	0.529	0.1751	00:12:29

traditional

model	val_acc	val_f1	train_time(one titan x)
traditional_bilstm_word_w2v_data_tune	0.9115	0.9097	00:09:07
traditional_bilstm_word_w2v_data_tune_0.48	0.913	0.9116	-
traditional_bilstm_word_w2v_data_tune_0.46	0.912	0.9108	-
traditional_cnnrnn_word_w2v_data_tune	0.908	0.9079	00:07:07
traditional_dcnn_word_w2v_data_tune	0.908	0.9089	00:02:17
traditional_dpcnn_word_w2v_data_tune	0.907	0.9067	00:01:06
traditional_han_word_w2v_data_tune	0.902	-	00:07:18
traditional_multicnn_word_w2v_data_tune	0.5	0.6667	00:01:30
traditional_rcnn_word_w2v_data_tune	0.912	0.9110	00:07:56
traditional_rnncnn_word_w2v_data_tune	0.9095	-	00:06:38
traditional_cnn_word_w2v_data_tune	0.909	0.9084	00:01:00
traditional_vdcnn_word_w2v_data_tune	0.885	0.8864	00:13:08
traditional_bilstm_word_w2v_data_fix	0.8475	0.8508	00:15:56
traditional_dcnn_word_w2v_data_fix	0.834	0.8302	00:02:34
traditional_dpcnn_word_w2v_data_fix	0.839	0.8382	00:01:02
traditional_han_word_w2v_data_fix	0.8535	0.8539	00:11:47
traditional_multicnn_word_w2v_data_fix	0.5	0.6667	00:01:26
traditional_rcnn_word_w2v_data_fix	0.8545	0.8522	00:12:38
traditional_rnncnn_word_w2v_data_fix	0.8505	0.8510	00:10:31
traditional_cnn_word_w2v_data_fix	0.842	0.8326	00:01:13
traditional_vdcnn_word_w2v_data_fix	0.8005	0.7944	00:11:22
traditional_bilstm_char_w2v_data_tune	0.8635	0.8616	00:24:57
traditional_dcnn_char_w2v_data_tune	0.8705	0.8684	00:04:57
traditional_dpcnn_char_w2v_data_tune	0.884	0.8850	00:02:59
traditional_han_char_w2v_data_tune	0.5	0.0	00:13:05
traditional_multicnn_char_w2v_data_tune	0.5	0.0	00:02:32
traditional_rcnn_char_w2v_data_tune	0.866	0.8640	00:19:07
traditional_rnncnn_char_w2v_data_tune	0.8665	0.8607	00:19:32
traditional_cnn_char_w2v_data_tune	0.878	0.8803	00:02:41
traditional_vdcnn_char_w2v_data_tune	0.5015	0.0404	00:08:50
traditional_bilstm_char_w2v_data_fix	0.825	0.8234	00:44:19
traditional_dcnn_char_w2v_data_fix	0.781	0.768	00:06:27
traditional_dpcnn_char_w2v_data_fix	0.8055	0.8052	00:02:27
traditional_han_char_w2v_data_fix	0.5	0.0	00:12:30
traditional_multicnn_char_w2v_data_fix	0.5	0.00	00:02:28
traditional_rcnn_char_w2v_data_fix	0.8145	0.8179	00:33:37
traditional_rnncnn_char_w2v_data_fix	0.8025	0.8045	00:27:34
traditional_cnn_char_w2v_data_fix	0.823	0.8281	00:03:52
traditional_vdcnn_char_w2v_data_fix	0.5	0.0176	00:07:40

conclusion

word level input is better than charracter level input
word2vec is better than fasttext and glove
fine-tuning word embeddings is better than fixing word embeddings
BiLSTM performs best, but other models except vdccn and multicnn performs very close.
data agumentaion doesn't help.
skip ngram doesn't help

Performance of ml based model

Simplified

model	val_acc	val_f1	val_p	val_r
simplified_svm_binary_char_(1, 1)	0.8115	0.8103	0.8156	0.805
simplified_svm_binary_char_(2, 2)	0.862	0.8602	0.8717	0.849
simplified_svm_binary_char_(3, 3)	0.876	0.8749	0.8829	0.867
simplified_svm_binary_char_(4, 4)	0.853	0.8498	0.8684	0.832
simplified_svm_binary_char_(5, 5)	0.816	0.8073	0.8473	0.771
simplified_svm_binary_char_(6, 6)	0.7905	0.7655	0.8691	0.684
simplified_svm_binary_char_(1, 3)	0.8775	0.8766	0.8832	0.87
simplified_svm_binary_char_(2, 3)	0.879	0.8780	0.8852	0.871
simplified_svm_binary_word_(1, 1)	0.8385	0.8341	0.8574	0.812
simplified_svm_binary_word_(2, 2)	0.7075	0.6118	0.9093	0.461
simplified_svm_binary_word_(3, 3)	0.5515	0.1882	0.9905	0.104
simplified_svm_binary_word_(4, 4)	0.515	0.0582	1.0	0.03
simplified_svm_binary_word_(5, 5)	0.5035	0.6682	0.5018	1.0
simplified_svm_binary_word_(6, 6)	0.5015	0.6673	0.5008	1.0
simplified_svm_binary_char_(2, 3)word(1, 1)	0.877	0.8760	0.8831	0.869
simplified_svm_tf_char_(3, 3)	0.8705	0.8694	0.8769	0.862
simplified_svm_tf_char_(2, 3)	0.881	0.8791	0.8928	0.866
simplified_svm_tf_word_(1, 1)	0.837	0.8337	0.8510	0.817
simplified_svm_tf_char_(2, 3)word(1, 1)	0.88	0.8787	0.8877	0.87
simplified_svm_tfidf_char_(3, 3)	0.8935	0.8926	0.8994	0.886
simplified_svm_tfidf_char_(2, 3)	0.8955	0.8946	0.9023	0.887
simplified_svm_tfidf_word_(1, 1)	0.851	0.8482	0.8641	0.833
simplified_svm_tfidf_char_(2, 3)word(1, 1)	0.8885	0.8881	0.8912	0.885
simplified_sgd_binary_char_(3, 3)	0.8725	0.8709	0.8821	0.86
simplified_sgd_binary_char_(2, 3)	0.872	0.8698	0.8851	0.855
simplified_sgd_binary_word_(1, 1)	0.8455	0.8446	0.8493	0.84
simplified_sgd_binary_char_(2, 3)word(1, 1)	0.8785	0.8759	0.8946	0.858
simplified_sgd_tf_char_(3, 3)	0.8655	0.8582	0.9075	0.814
simplified_sgd_tf_char_(2, 3)	0.865	0.8653	0.8635	0.867
simplified_sgd_tf_word_(1, 1)	0.8495	0.8477	0.8577	0.838
simplified_sgd_tf_char_(2, 3)word(1, 1)	0.864	0.8653	0.8568	0.874
simplified_sgd_tfidf_char_(3, 3)	0.876	0.8745	0.8852	0.864
simplified_sgd_tfidf_char_(2, 3)	0.88	0.8777	0.8950	0.861
simplified_sgd_tfidf_word_(1, 1)	0.853	0.8469	0.8837	0.813
simplified_sgd_tfidf_char_(2, 3)word(1, 1)	0.8875	0.8866	0.8934	0.88
simplified_lr_binary_char_(1, 1)	0.8185	0.8167	0.8246	0.809
simplified_lr_binary_char_(2, 2)	0.872	0.8701	0.8835	0.857
simplified_lr_binary_char_(3, 3)	0.879	0.8775	0.8883	0.867
simplified_lr_binary_char_(4, 4)	0.8505	0.8459	0.8725	0.821
simplified_lr_binary_char_(2, 3)	0.8865	0.8854	0.8939	0.877
simplified_lr_binary_char_(1, 3)	0.886	0.8847	0.8947	0.875
simplified_lr_binary_word_(1, 1)	0.859	0.8545	0.8827	0.828
simplified_lr_binary_word_(2, 2)	0.706	0.6111	0.9023	0.462
simplified_lr_binary_word_(3, 3)	0.552	0.1899	0.9905	0.105
simplified_lr_binary_char_(2, 3)word(1, 1)	0.8875	0.8865	0.8942	0.879
simplified_mnb_binary_char_(1, 1)	0.8225	0.8222	0.8235	0.821
simplified_mnb_binary_char_(2, 2)	0.8935	0.8942	0.8885	0.9
simplified_mnb_binary_char_(3, 3)	0.9015	0.9035	0.8859	0.922
simplified_aug_mnb_binary_char_(3, 3)	0.8985	0.9007	0.8813	0.921
simplified_mnb_binary_char_(4, 4)	0.8835	0.8855	0.8705	0.901
simplified_mnb_binary_char_(1, 3)	0.903	0.9040	0.8951	0.913
simplified_mnb_binary_char_(2, 3)	0.908	0.9094	0.8953	0.924
simplified_mnb_binary_char_(2, 3)_0.46	0.9095	0.91114	0.8949	0.928
simplified_mnb_binary_char_(2, 3)_0.48	0.9095	0.91105	0.89565	0.927
simplified_aug_mnb_binary_char_(2, 3)	0.91	0.9111	0.8996	0.923
simplified_mnb_binary_word_(1, 1)	0.878	0.8790	0.8720	0.886
simplified_mnb_binary_word_(2, 2)	0.709	0.6141	0.9114	0.463
simplified_mnb_binary_word_(3, 3)	0.552	0.1898	0.9905	0.105
simplified_aug_mnb_binary_word_(1, 1)	0.872	0.8756	0.8516	0.901
simplified_mnb_binary_char_(2, 3)word(1, 1)	0.9055	0.9070	0.8925	0.922
simplified_aug_mnb_binary_char_(2, 3)word(1, 1)	0.906	0.9076	0.8926	0.923
simplified_mnb_tf_char_(3, 3)	0.901	0.9030	0.8848	0.922
simplified_mnb_tf_char_(2, 3)	0.906	0.9077	0.8919	0.924
simplified_mnb_tf_word_(1, 1)	0.8795	0.8800	0.8761	0.884
simplified_mnb_tf_char_(2, 3)word(1, 1)	0.9035	0.9052	0.8898	0.921
simplified_mnb_tfidf_char_(3, 3)	0.8945	0.8969	0.8768	0.918
simplified_mnb_tfidf_char_(2, 3)	0.8995	0.9011	0.8867	0.916
simplified_mnb_tfidf_word_(1, 1)	0.873	0.8745	0.8643	0.885
simplified_mnb_tfidf_char_(2, 3)word(1, 1)	0.895	0.8970	0.8805	0.914

traditional

model	val_acc	val_f1	val_p	val_r
traditional_svm_binary_char_(1, 1)	0.8325	0.8321	0.8342	0.83
traditional_svm_binary_char_(2, 2)	0.8765	0.8747	0.8877	0.862
traditional_svm_binary_char_(3, 3)	0.883	0.8821	0.8892	0.875
traditional_svm_binary_char_(4, 4)	0.86	0.8574	0.8734	0.842
traditional_svm_binary_char_(1, 3)	0.894	0.8930	0.9012	0.885
traditional_svm_binary_char_(1, 3)	0.8895	0.8891	0.8922	0.886
traditional_svm_binary_word_(1, 1)	0.8435	0.8412	0.8538	0.829
traditional_svm_binary_word_(2, 2)	0.657	0.7380	0.5970	0.966
traditional_svm_binary_word_(3, 3)	0.54	0.6850	0.5208	1.0
traditional_svm_binary_char_(2, 3)word(1, 1)	0.897	0.8963	0.9026	0.89
traditional_lr_binary_char_(1, 1)	0.8455	0.8443	0.8508	0.838
traditional_lr_binary_char_(2, 2)	0.889	0.8874	0.9002	0.875
traditional_lr_binary_char_(3, 3	0.884	0.8825	0.8943	0.871
traditional_lr_binary_char_(4, 4)	0.857	0.8529	0.8782	0.829
traditional_lr_binary_char_(2, 3)	0.896	0.8947	0.9057	0.884
traditional_lr_binary_char_(1, 3)	0.899	0.8979	0.9080	0.888
traditional_lr_binary_word_(1, 1)	0.8635	0.8591	0.8879	0.832
traditional_lr_binary_word_(2, 2)	0.707	0.6124	0.9043	0.463
traditional_lr_binary_word_(3, 3)	0.552	0.1899	0.9906	0.105
traditional_lr_binary_char_(2, 3)word(1, 1)	0.899	0.8979	0.9071	0.889
traditional_mnb_binary_char_(1, 1)	0.848	0.8486	0.8452	0.852
traditional_mnb_binary_char_(2, 2)	0.91	0.9104	0.9059	0.915
traditional_mnb_binary_char_(3, 3)	0.915	0.9166	0.8998	0.934
traditional_mnb_binary_char_(4, 4)	0.891	0.8930	0.8767	0.91
traditional_aug_mnb_binary_char_(3, 3)	0.9105	0.9122	0.8951	0.93
traditional_mnb_binary_char_(2, 3)	0.9225	0.9234	0.9130	0.934
traditional_mnb_binary_char_(2, 3)_0.46	0.9225	0.9235	0.9122	0.935
traditional_mnb_binary_char_(2, 3)_0.48	0.9225	0.9234	0.9130	0.934
traditional_mnb_binary_char_(1, 3)	0.917	0.9176	0.9112	0.924
traditional_aug_mnb_binary_char_(2, 3)	0.923	0.9237	0.9155	0.932
traditional_mnb_binary_word_(1, 1)	0.8855	0.8864	0.8791	0.894
traditional_aug_mnb_binary_word_(1, 1)	0.8795	0.8829	0.8584	0.909
traditional_mnb_binary_word_(2, 2)	0.71	0.6154	0.9134	0.464
traditional_mnb_binary_word_(3, 3)	0.552	0.1899	0.9905	0.105
traditional_mnb_binary_char_(2, 3)word(1, 1)	0.92	0.9211	0.9085	0.934
traditional_aug_mnb_binary_char_(2, 3)word(1, 1)	0.918	0.9192	0.9058	0.933

conclusion

char trigram is better than char unigram and char bigram, word unigram is better than word bigram and word trigram
combine bigram and trigram helps, but further combine word unigram doesn't make a difference
binary vectors, tf weighted vectors and tf-idf weighted vectors have very close performace
navie bayers is a very strong classifier on this task
data agumentation doesn't make a difference
skip ngram doeen't help, so does pos ngram

Performance of dialect matching model

Not helping.

Performance of Ensemble-based model

simplified

ensemble_model	ensemble_type	val_acc	val_f1	val_p	val_r
bilstm_word mnb_binary_char_(2, 3)	mean	0.913	0.9137	0.9065	0.921
bilstm_word mnb_binary_char_(2, 3)_0.46	mean	0.9135	0.9148	0.9011	0.929
bilstm_word mnb_binary_char_(2, 3)_0.48	mean	0.9135	0.9144	0.9050	0.924
bilstm_word mnb_bianry_char_(2, 3)	max	0.913	0.9137	0.9065	0.921
bilstm_word mnb_binary_char_(2, 3)_0.46	max	0.913	0.9137	0.9065	0.921
bilstm_word mnb_binary_char_(2, 3)_0.48	max	0.913	0.9137	0.9065	0.921
bilstm_word mnb_bianry_char_(2, 3)	vote	0.908	0.9094	0.8953	0.924
bilstm_word mnb_binary_char_(2, 3)_0.4	xgboost	0.9105	0.9114	0.9020	0.921
bilstm_word mnb_binary_char_(2, 3)_0.4	svm	0.9065	0.9067	0.9044	0.909
bilstm_word mnb_binary_char_(2, 3)_0.4	lda	0.906	0.9061	0.9044	0.908
bilstm_word mnb_binary_char_(2, 3)	xgboost	0.9075	0.9076	0.9062	0.909
bilstm_word mnb_binary_char_(2, 3)	svm	0.9065	0.9067	0.9044	0.909
bilstm_word mnb_binary_char_(2, 3)	lda	0.9055	0.9056	0.9043	0.907
svm_lr_mnb_binary_char_(2, 3)	gnb	0.8915	0.8910	0.8951	0.887
svm_lr_mnb_binary_char_(2, 3)	mnb	0.903	0.9063	0.8759	0.939
svm_lr_mnb_binary_char_(2, 3)_0.52	mnb	0.905	0.9079	0.8842	0.932
svm_lr_mnb_binary_char_(2, 3)_0.56	mnb	0.907	0.907	0.907	0.907
svm_lr_mnb_binary_char_(2, 3)	mean	0.9025	0.9029	0.8989	0.907
svm_lr_mnb_binary_char_(2, 3)	max	0.908	0.9089	0.9	0.918
svm_lr_mnb_binary_char_(2, 3)	vote	0.888	0.8869	0.8959	0.878
svm_lr_mnb_binary_char_(2, 3)_0.4	max	0.91	0.9111	0.9003	0.922
all_dl_model	gnb	0.9005	0.9017	0.8907	0.913
all_dl_model_0.56	lr	0.9015	0.9010	0.9060	0.896
all_dl_model	mean	0.905	0.9046	0.9083	0.901
all_dl_model	max	0.9015	0.9010	0.9060	0.896
all_dl_model	vote	0.906	0.9057	0.9085	0.903

traditional

ensemble_model	ensemble_type	val_acc	val_f1	val_p	val_r
bilstm_word mnb_binary_char_(2, 3)	mean	0.924	0.9242	0.9223	0.926
bilstm_word mnb_binary_char_(2, 3)_0.46	mean	0.925	0.9257	0.9166	0.935
bilstm_word mnb_binary_char_(2, 3)_0.48	mean	0.926	0.9264	0.9218	0.931
bilstm_word mnb_bianry_char_(2, 3)	max	0.924	0.9242	0.9223	0.926
bilstm_word mnb_binary_char_(2, 3)_0.46	max	0.924	0.9242	0.9223	0.926
bilstm_word mnb_binary_char_(2, 3)_0.48	max	0.924	0.9242	0.9223	0.926
bilstm_word mnb_bianry_char_(2, 3)	vote	0.9225	0.9234	0.9130	0.934
bilstm_word mnb_binary_char_(2, 3)_0.46	vote	0.924	0.9242	0.9223	0.926
bilstm_word mnb_binary_char_(2, 3)_0.48	vote	0.924	0.9242	0.9223	0.926
bilstm_word mnb_binary_char_(2, 3)	gnb	0.9215	0.9216	0.9202	0.923
svm_lr_mnb_binary_char_(2, 3)	mean	0.917	0.9171	0.9162	0.918
svm_lr_mnb_binary_char_(2, 3)	max	0.9225	0.9231	0.9162	0.93
svm_lr_mnb_binary_char_(2, 3)_0.4	max	0.924	0.9247	0.9165	0.933
svm_lr_mnb_binary_char_(2, 3)	vote	0.8985	0.8976	0.9054	0.89
svm_lr_mnb_binary_char_(2, 3)	5B33 gnb	0.906	0.906	0.906	0.906
svm_lr_mnb_binary_char_(2, 3)	mnb	0.918	0.9205	0.8936	0.949
svm_lr_mnb_binary_char_(2, 3)_0.54	mnb	0.92	0.9209	0.9101	0.932
svm_lr_mnb_binary_char_(2, 3)_0.56	mnb	0.922	0.9221	0.9212	0.923
all_dl_model	gnb	0.9155	0.9161	0.9094	0.923
all_dl_model	mean	0.9215	0.9207	0.9297	0.912
all_dl_model	max	0.91	0.9090	0.9192	0.899
all_dl_model	vote	0.9195	0.9185	0.9303	0.907

Performance of shared task

simplified

model	acc	f1_mocro	f1_macro	f1_weighted
simplified_bilstm_word_w2v_data_tune	0.812000	0.812000	0.811795	0.81179
simplified_mnb_binary_char_(2, 3)	0.850500	0.850500	0.849895	0.849895
simplified_bilstm_mnb_mean_ensemble	0.853500	0.853500	0.853031	0.853031

traditional

model	acc	f1_mocro	f1_macro	f1_weighted
simplified_bilstm_word_w2v_data_tune	0.845000	0.845000	0.844965	0.844965
simplified_mnb_binary_char_(2, 3)	0.865500	0.865500	0.865022	0.865022
simplified_bilstm_mnb_mean_ensemble	0.869000	0.869000	0.868710	0.868710

Name		Name	Last commit message	Last commit date
Latest commit History 12 Commits
img		img
layers		layers
models		models
paper/dmt-latex		paper/dmt-latex
predict		predict
raw_data		raw_data
utils		utils
.gitignore		.gitignore
README.md		README.md
config.py		config.py
data_analysis.md		data_analysis.md
ensemble_models.py		ensemble_models.py
prepare_test_input.py		prepare_test_input.py
preprocess.py		preprocess.py
train.py		train.py

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