CS3602 Slot Language Understanding

Benhao Huang, Yizhou Liu and Pengxiang Zhu

Computer Science and Engineering of IEEE Honor Class, Shanghai Jiao Tong University

In this repo, we complete the final project for CS3602 (Natural Language Understanding). The dataset data/train.json is used for training and data/developement.json is used for testing. The project is about slot language understanding, where the datasets contain (action, slot, value) triples and we need to extract these information from the given ASR inputs. The framework of our proposed model can be shown as follows.

Environment Setup

• Build the basic conda environment

conda env create -f environment.yaml

• Install the dependencies

pip install -r requirements.txt

• Install text2vec

pip install -U text2vec

Usage

For training on the given dataset, run the following command in shell

python scripts/slu_fused_bert.py --expri=<name of the experiment> --device=<gpu_id, -1 for cpu>

For other arguments, refer to utils/args.py for modification. The training results will be stored in ./checkpoints, ./visualization. If --get_wrong_examples is set to true, then you can find the wrong examples in ./wrong_examples.

For testing on the given dataset, run the following command in shell

python scripts/slu_fused_bert.py --expri=<name of the experiment> --device=<gpu_id, -1 for cpu> --testing --ckpt <path to checkpoint>

The pretrained model can be downloaded here (Google Drive).

Result

It is worth noting that it is therotically impossible to predict the correct result for 12% of the test data, as the value of the label is not in the asr input. Since we can only extract the value from the asr input, predicting the correct result would require generative methods, which are not available in our model. Empircal results of the model is shown as follows, while the prediction of the given test_unlabelled.json is stored in prediction.json.