This is the official code for our paper A Split-and-Recombine Approach for Follow-up Query Analysis (EMNLP2019).

In this paper, we propose to achieve context-dependent semantic parsing via performing follow-up query analysis, which aims to restate context-dependent natural language queries with contextual information. To accomplish the task, we propose the approach Split-And-Recombine(StAR), a novel approach with a well-designed two-phase process.

• Setup
• Preprocess Data
• Training Arguments
• Pretrain Model
• Train Model via Reinforcement Learning
• Expected Result
• Question
• Cite
• Contributing

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

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)

The main requirement of our code base is as following:

• allennlp == 0.8.2
• pytorch >= 0.4.0
• nltk >= 3.4.5

You could install them via pip commands. We recommend you to install pytorch first. You could follow this guide. Our code base can be trained on GPU/CPU as you like because the bottleneck is on computing the reward of reinforcement learning. If you want to speed up the training and you have enough CPU cores, you can manually modify the number of threads or make the reward collects from parallel processes at here.

Then you could install other dependencies by requirements.txt:

pip install -r requirements.txt

All command arguments are list as following:

usage: train_model.py [-h] [--learning_rate LEARNING_RATE] --store_folder
                      {pretrain,reinforce}
                      [--serialization_dir SERIALIZATION_DIR] [--seed SEED]
                      [--rl_basic RL_BASIC] --margin MARGIN
                      [--patience PATIENCE]
                      [--validation_metric {overall,symbol,bleu}]
                      [--epoch EPOCH]

Training the FollowUpSnippet Model

optional arguments:
  -h, --help            show this help message and exit
  --learning_rate LEARNING_RATE
                        learning rate for reinforcement learning or pretrain
  --training_mode {pretrain,reinforce}
                        Specify the training mode from pretrain or reinforce.
  --serialization_dir SERIALIZATION_DIR
                        The checkpoint folder which stores all training
                        states/model states and metrics.
  --seed SEED           The seed for reproducing the experiments.
  --rl_basic RL_BASIC   pretrained checkpoint dir for reinforcement
                        learning training
  --margin MARGIN       margin hyper-parameter for margin loss.
  --patience PATIENCE   patience of validation.
  --validation_metric {overall,symbol,bleu}
                        metric keeps the best model in validation.
  --epoch EPOCH         maximum training epochs

Before the training, we should prepare the processed data using preprocess.py. The first run of preprocessing may be slow, but then the data will be cacahed into the cache folder.

Note the script will automatically download data files from FollowUp dataset and glove pre-trained embedding.

python preprocess.py

Then you get the log as:

Start to download FollowUp dataset from https://github.com/SivilTaram/FollowUp
Successfully unzip the FollowUp dataset and process it into `data_processed` and `data`.
Start to download Glove.twitter.27B from http://nlp.stanford.edu/data/glove.twitter.27B.zip
Start to unzip glove.zip to get the glove pre-training embedding.
Successfully unzip `glove.zip` into `glove` folder.
800it [00:03, 243.80it/s]
200it [00:00, 278.25it/s]
100%|██████████| 1000/1000 [00:00<00:00, 9142.40it/s]

Once the data ready, we can train a pre-training model as stated in the paper using processed data data_processed. For windows users, you could replace \ with ^ and it will work.

python train_model.py \
       --training_mode = pretrain \
       --learning_rate = 1e-3 \
       --margin = 0.3 \
       --epoch = 20 \
       --serialization_dir=split_and_recombine

Then we could train the Split-And-Recombine model. The training script will use the best model state in pretrain folder automatically. The default validation metric is overall, the average of symbol accuracy and bleu score.

python train_model.py \
       --training_mode = reinforce \
       --learning_rate = 1e-4 \
       --margin = 0.3 \
       --patience = 30 \
       --epoch = 80 \
       --rl_basic = split_and_recombine

Expected result is as stated in the paper, where BLEU is ~67.05(+-1.05) and Symbol Acc is ~54.00(+-1.09). The training will last ~6 hours on a Tesla M40 GPU. We provide partial log information in log.txt for check. And the dev performance curve is as (the plot includes the result on pre-training):

If you have any question or find any bug, please go ahead and open an issue. Issues are an acceptable discussion forum as well.

If you want to concat the author, please email: qian DOT liu AT buaa.edu.cn

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

@inproceedings{qian2019star,
  title={A Split-and-Recombine Approach for Follow-up Query Analysis},
  author={Qian, Liu and Bei, Chen and Haoyan, Liu and Jian-Guang, Lou and Lei, Fang and Bin, Zhou and Dongmei, Zhang},
  booktitle={Proceedings of EMNLP},
  year={2019}
}

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