mishra-sid / JointBERT-with-syntax

Add syntactic features to JointBERT: "BERT for Joint Intent Classification and Slot Filling"

JointBERT-with-syntax

Adding Syntactic Features to JointBERT: BERT for Joint Intent Classification and Slot Filling

Model Architecture

• Predict intent and slot at the same time from one BERT model (=Joint model)
• total_loss = intent_loss + coef * slot_loss (Change coef with --slot_loss_coef option)
• If you want to use CRF layer, give --use_crf option

Dependencies

• python
• torch
• transformers
• seqeval
• pytorch-crf

Dataset

	Train	Dev	Test	Intent Labels	Slot Labels
ATIS	4,478	500	893	21	120
Snips	13,084	700	700	7	72

• The number of labels are based on the train dataset.
• Add UNK for labels (For intent and slot labels which are only shown in dev and test dataset)
• Add PAD for slot label

Generate Data

$ python generate_data.py --input_dir data/snips --output_dir data --setting bracketed.ground_truth

# To generate all at once 
$ ./bin/generate_all_data.sh

Training & Evaluation

$ python3 train.py --task {task_name} \
                  --model_type {model_type} \
                  --model_dir {model_dir_name} \
                  --do_train --do_eval \
                  --use_crf

# For ATIS
$ python3 train.py --task atis \
                  --model_type bert \
                  --model_dir atis_model \
                  --do_train --do_eval
# For Snips
$ python3 train.py --task snips \
                  --model_type bert \
                  --model_dir snips_model \
                  --do_train --do_eval

Prediction

$ python3 predict.py --input_file {INPUT_FILE_PATH} --output_file {OUTPUT_FILE_PATH} --model_dir {SAVED_CKPT_PATH}

Results

	intent_acc	slot_precision	slot_recall	slot_f1	semantic_frame_acc
('snips', 'baseline')	98.1905	94.8084	95.9032	95.3526	89.5714
('snips', 'bracketed.NP+VP.supervised')	98.4762	94.9014	96.0149	95.4549	89.4286
('snips', 'bracketed.NP+VP.with_labels.supervised')	98.381	94.6116	95.7914	95.1977	88.7143
('snips', 'bracketed.NP.supervised')	98.1429	94.7232	95.9032	95.3089	89.0952
('snips', 'bracketed.VP.supervised')	98	94.7945	95.959	95.373	89.3333
('snips', 'bracketed.full.supervised')	98.0476	91.8405	94.1069	92.9591	85.1905
('snips', 'bracketed.full.with_labels.supervised')	98.3333	92.6653	94.6335	93.6386	86
('snips', 'bracketed.ground_truth')	98.5238	97.5508	97.8771	97.7136	94.8571
('snips', 'control.less_than_avg_length')	97.9048	89.3319	92.9236	91.092	80.9524
('snips', 'control.less_than_avg_length.bracketed.full.supervised')	96.8095	85.4795	90.077	87.7147	74.6667
('snips', 'control.random_50pct')	97.8095	93.6102	94.9348	94.2678	86.2381
('snips', 'control.random_50pct.bracketed.full.supervised')	97.8095	87.9652	91.8793	89.8741	79.5238
('atis', 'baseline')	97.2751	94.6374	95.3153	94.9751	86.2262
('atis', 'bracketed.NP+VP.supervised')	97.723	95.1964	95.7685	95.4815	87.3087
('atis', 'bracketed.NP+VP.with_labels.supervised')	97.6857	95.2127	95.8746	95.5424	87.7566
('atis', 'bracketed.NP.no_nest.supervised')	98.2381	94.8164	96.0521	95.4301	89.619
('atis', 'bracketed.NP.supervised')	97.723	94.2898	95.2331	94.759	85.9276
('atis', 'bracketed.VP.supervised')	97.4991	95.1215	95.7247	95.4221	87.6073
('atis', 'bracketed.full.supervised')	97.3871	94.2687	94.9305	94.5983	86.7861
('atis', 'bracketed.full.with_labels.supervised')	97.1258	93.4811	94.3472	93.912	86.0769
('atis', 'bracketed.ground_truth')	97.5737	95.8782	96.2271	96.0522	89.4364
('atis', 'control.less_than_avg_length')	97.3124	93.7032	94.646	94.1719	84.6211
('atis', 'control.less_than_avg_length.bracketed.full.supervised')	97.2004	92.094	93.3603	92.7227	84.6958
('atis', 'control.random_50pct')	97.3124	94.5388	95.327	94.9312	85.9649
('atis', 'control.random_50pct.bracketed.full.supervised')	96.4166	91.2556	92.2237	91.737	81.1497

Updates

• 2021/12/09: Add Generate data / training helper scripts

References

• Huggingface Transformers
• pytorch-crf