RE2

This is the original Tensorflow implementation of the ACL 2019 paper Simple and Effective Text Matching with Richer Alignment Features. Pytorch implementation: https://github.com/alibaba-edu/simple-effective-text-matching-pytorch.

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Setup
Usage

Simple and Effective Text Matching

RE2 is a fast and strong neural architecture for general purpose text matching applications. In a text matching task, a model takes two text sequences as input and predicts their relationship. This method aims to explore what is sufficient for strong performance in these tasks. It simplifies or omits many slow components which are previously considered as core building blocks in text matching. It achieves its performance by a simple idea, which is keeping three key features directly available for inter-sequence alignment and fusion: previous aligned features (Residual vectors), original point-wise features (Embedding vectors), and contextual features (Encoder output).

RE2 achieves performance on par with the state of the art on four benchmark datasets: SNLI, SciTail, Quora and WikiQA, across tasks of natural language inference, paraphrase identification and answer selection with no or few task-specific adaptations. It has at least 6 times faster inference speed compared with similarly performed models.

The following table lists major experiment results. The paper reports the average and standard deviation of 10 runs and the results can be easily reproduced. Inference time (in seconds) is measured by processing a batch of 8 pairs of length 20 on Intel i7 CPUs. The computation time of POS features used by CSRAN and DIIN is not included.

Model	SNLI	SciTail	Quora	WikiQA	Inference Time
BiMPM	86.9	-	88.2	0.731	0.05
ESIM	88.0	70.6	-	-	-
DIIN	88.0	-	89.1	-	1.79
CSRAN	88.7	86.7	89.2	-	0.28
RE2	88.9±0.1	86.0±0.6	89.2±0.2	0.7618 ±0.0040	0.03~0.05

Refer to the paper for more details of the components and experiment results.

Setup

install python >= 3.6 and pip
pip install -r requirements.txt
install Tensorflow 1.4 or above (the wheel file for Tensorflow 1.4 gpu version under python 3.6 can be found here)
Download GloVe word vectors (glove.840B.300d) to resources/

Data used in the paper are prepared as follows:

SNLI

Download and unzip SNLI (pre-processed by Tay et al.) to data/orig.
Unzip all zip files in the "data/orig/SNLI" folder. (cd data/orig/SNLI && gunzip *.gz)
cd data && python prepare_snli.py

SciTail

Download and unzip SciTail dataset to data/orig.
cd data && python prepare_scitail.py

Quora

Download and unzip Quora dataset (pre-processed by Wang et al.) to data/orig.
cd data && python prepare_quora.py

WikiQA

Download and unzip WikiQA to data/orig.
cd data && python prepare_wikiqa.py
Download and unzip evaluation scripts. Use the make -B command to compile the source files in qg-emnlp07-data/eval/trec_eval-8.0. Move the binary file "trec_eval" to resources/.

Usage

To train a new text matching model, run the following command:

python train.py $config_file.json5

Example configuration files are provided in configs/:

configs/main.json5: replicate the main experiment result in the paper.
configs/robustness.json5: robustness checks
configs/ablation.json5: ablation study

The instructions to write your own configuration files:

[
    {
        name: 'exp1', // name of your experiment, can be the same across different data
        __parents__: [
            'default', // always put the default on top
            'data/quora', // data specific configurations in `configs/data`
            // 'debug', // use "debug" to quick debug your code  
        ],
        __repeat__: 5,  // how may repetitions you want
        blocks: 3, // other configurations for this experiment 
    },
    // multiple configurations are executed sequentially
    {
        name: 'exp2', // results under the same name will be overwritten
        __parents__: [
            'default', 
            'data/quora',
        ],
        __repeat__: 5,  
        blocks: 4, 
    }
]

To check the configurations only, use

python train.py $config_file.json5 --dry

Citation

Please cite the ACL paper if you use RE2 in your work:

@inproceedings{yang2019simple,
  title={Simple and Effective Text Matching with Richer Alignment Features},
  author={Yang, Runqi and Zhang, Jianhai and Gao, Xing and Ji, Feng and Chen, Haiqing},
  booktitle={Association for Computational Linguistics (ACL)},
  year={2019}
}

License

RE2 is under Apache License 2.0.

pquochuy/simple-effective-text-matching

RE2