Skip to content

Latest commit

 

History

History
152 lines (109 loc) · 6.45 KB

README.md

File metadata and controls

152 lines (109 loc) · 6.45 KB
Raw

Incomplete Utterance Rewriting

中文版

The official pytorch implementation of our paper Incomplete Utterance Rewriting as Semantic Segmentation.

If you find our code useful for you, please consider citing our paper:

@inproceedings{qian2020incomplete,
  title={Incomplete Utterance Rewriting as Semantic Segmentation},
  author={Liu, Qian and Chen, Bei and Lou, Jian-Guang and Zhou, Bin and Zhang, Dongmei},
  booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing},
  year={2020}
}

Content

Requirement

Python Environment

First of all, you should setup a python environment. This code base has been tested under python 3.x, and we officially support python 3.7.

After installing python 3.7, we strongly recommend you to use virtualenv (a tool to create isolated Python environments) to manage the python environment. You could use following commands to create a environment.

python -m pip install virtualenv
virtualenv venv

Activate Virtual Environment

Then you should activate the environment to install the dependencies. You could achieve it via using the command as below. (Please change $ENV_FOLDER to your own virtualenv folder path, e.g. venv)

$ENV_FOLDER\Scripts\activate.bat (Windows)
source $ENV_FOLDER/bin/activate (Linux)

Install Libraries

The most important requirements of our code base are as following:

  • pytorch >= 1.2.0 (not tested on other versions, but 1.0.0 may work though)
  • allennlp == 0.9.0

Other dependencies can be installed by

pip install -r requirement.txt

Data

Prepare Dataset

Although we cannot provide dataset resources (copyright issue) in our repo, we provide download.sh for automatically downloading and preprocessing datasets used in our paper.

Here the preprocessing does not include exporting the distant supervision, a.k.a. the word-level edit matrix, used in our paper. Anyone interested in the distant supervision can focus on the dataset reader file src/data_reader.py (line 178-200).

Prepare Glove

If you want to train models on English datasets (i.e. Task and CANARD), please download Glove 6B. Unzip and move the glove.6B.100d.txt file into the folder glove.

Train

You could train models on different datasets using *.sh files under the src folder.

For example, you could train RUN + BERT on Multi by running the following command under the src folder as:

./train_multi_bert.sh

Configs Table

Config Model in Paper
canard.jsonnet RUN on CANARD (Elgohary et al. 2019)
multi.jsonnet RUN on Multi (Pan et al. 2019)
multi_bert.jsonnet RUN + BERT on Multi (Pan et al. 2019)
rewrite.jsonnet RUN on Rewrite (Su et al. 2019)
rewrite_bert.jsonnet RUN + BERT on Rewrite (Su et al. 2019)
task.jsonnet RUN on Task (Quan et al. 2019)

Tips for training

  1. If you does not rely on BLEU metrics to pick up your best weight file on the dev set, you could disable it to achieve a faster evaluation speed.
  2. By default we do not calculate any metric on the train set to save training time, but you could enable it by setting enable_training_log as True in *.jsonnet (Refer readers to see task.jsonnet).
  3. All configs are tested successfully under Tesla M40 (24GB), and if there is any error such as CUDA Out Of Memory, you could solve it by reducing the hyper-parameter batch_size in *.jsonnet. In our experience, it will not hurt the performance by a large margin.

Evaluate

Once a model is well trained, allennlp will save a compressed model zip file which is usually named after model.tar.gz under the checkpoint folder. Our evaluation is based on it.

We provide a evaluate file under src folder, and you could evaluate a model file by running the following command:

python evaluate.py --model_file model.tar.gz --test_file ../dataset/Multi/test.txt

The above script will generate a file model.tar.gz.json which records the detailed performance. For example, the performance of RUN + BERT on Rewrite is:

{
    "ROUGE": 0.9394040084189113,
    "_ROUGE1": 0.961865057419486,
    "_ROUGE2": 0.9113051224617216,
    "EM": 0.688,
    "_P1": 0.9451903332806824,
    "_R1": 0.8668694770389685,
    "F1": 0.9043373129817137,
    "_P2": 0.8648273949812838,
    "_R2": 0.7989241803278688,
    "F2": 0.8305705345849144,
    "_P3": 0.8075098814229249,
    "_R3": 0.7449860216360763,
    "F3": 0.774988935954985,
    "_BLEU1": 0.9405510823944796,
    "_BLEU2": 0.9172718486250105,
    "_BLEU3": 0.8932687251641028,
    "BLEU4": 0.8691863201601382,
    "loss": 0.2084200546145439
}

Next, we will provide all pre-trained models to reproduce results reported in our paper. We recommend you to download them and put them into the folder pretrained_weights and run commands like below:

python evaluate.py --model_file ../pretrianed_weights/rewrite.tar.gz --test_file ../dataset/Multi/test.txt

Predict

We provide an easy function call to predict a rewriting sentence given a specific dialogue context in src/predict.py. You could follow it to customize your self.

Pre-trained Models

Dataset BERT Config EM Rewriting F1 BLEU4 Pretrained_Weights
Rewrite No rewrite.jsonnet 53.6 81.3 79.6 rewrite.tar.gz
Rewrite Yes rewrite_bert.jsonnet 68.8 90.4 86.9 rewrite_bert.tar.gz
CANARD No canard.jsonnet 18.3 44.2 49.8 canard.tar.gz
Multi No multi.jsonnet 43.3 60.7 81.1 multi.tar.gz
Multi Yes multi_bert.jsonnet 49.3 69.5 83.7 multi_bert.tar.gz