Source code of the ACL2019 paper "Simple and Effective Text Matching with Richer Alignment Features".
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.
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.
pip install -r requirements.txt
resources/
Data used in the paper are prepared as follows:
data/orig
.cd data/orig/SNLI && gunzip *.gz
)cd data && python prepare_snli.py
data/orig
.cd data && python prepare_scitail.py
data/orig
.cd data && python prepare_quora.py
data/orig
.cd data && python prepare_wikiqa.py
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/
.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 checksconfigs/ablation.json5
: ablation studyThe 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
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}
}
RE2 is under Apache License 2.0.