面向中文领域的轻量文本匹配框架，集成文本匹配，文本蕴含，释义识别等领域的各个经典，STA模型
- DSSM 【Learning Deep Structured Semantic Models for Web Search using Clickthrough Data，2013】
- ConvNet 【Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks，2015】
- QACNN 【Applying Deep Learning to Answer Selection: A Study And An Open Task，2015】
- APCNN 【Attentive Pooling Networks，2016】
- ABCNN 【ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs，2017】
- CAM 【A Compare-Aggregate Model for Matching Text Sequences，2017】
- ESIM 【Enhanced LSTM for Natural Language Inference，2017】
- BiMPM 【Bilateral Multi-Perspective Matching for Natural Language Sentences，2017】
- HCNN 【Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems，2017】
- HyperQA 【Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering，2018】
- MAN 【Multihop Attention Networks for QA Matching，2018】
- DIIN 【Natural Language Inference Over Interaction Space，2018】
- DRCN 【Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information，2018】
- TF 【Attention Is All You Need，2018】
- Bert 【BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding，2018】
- Xlnet 【XLNet: Generalized Autoregressive Pretraining for Language Understanding，2019】
- RE2 【Simple and Effective Text Matching with Richer Alignment Features，2019】
- ERCNN 【Enhanced Recurrent Convolutional Neural Networks for Learning Sentence Similarity，CCL 2019】
- GTF 【Gaussian Transformer: A Lightweight Approach for Natural Language Inference，2019】
- MVMT 【Many vs. Many Query Matching with Hierarchical BERT and Transformer，NLPCC 2019】
- HAS 【Hashing based Answer Selection，2019】
- GGSA 【Gated Group Self-Atention for Answer Selection，2019】
- Comp-Clip-LM 【A Compare-Aggregate Model with Latent Clustering for Answer Selection，CIKM 2019】
- BERT+GSAMN 【A Gated Self-attention Memory Network for Answer Selection，EMNLP 2019, STA】
| Model | Loss | Acc | Train_time | Test_size | Test_time | Best_epoch | 输入说明 | 论文地址 | 年份 |
|---|---|---|---|---|---|---|---|---|---|
| DSSM | 0.6441 | 0.6341 | 1877.230 s | 10000 | 1.524 s | 15 | 字向量 | DSSM | 2013 |
| ConvNet | 0.6702 | 0.6945 | 210.760 s | 10000 | 0.355 s | 6 | 字向量 | ConvNet | 2015 |
| ABCNN | 0.6153 | 0.75 | 2872.306 s | 10000 | 2.203 s | 29 | 字向量 | ABCNN | 2017 |
| ESIM | 0.5545 | 0.755 | 35399.251 s | 10000 | 53.762 s | 41 | 字向量 | ESIM | 2017 |
| ESIM | 0.5592 | 0.738 | 35489.816 s | 10000 | 65.915 s | 18 | 字向量+Attention_Mask | ESIM | 2017 |
| MAN | - | - | - | - | - | - | - | MAN | 2018 |
| BiMPM | 0.4783 | 0.761 | 320535.6 s | 10000 | 2070.67 s | 44 | 字向量+静态词向量 | BiMPM | 2017 |
| HCNN | 0.6196 | 0.726 | 2843.951 s | 10000 | 6.849 s | 35 | 字向量+动态词向量 | HCNN | 2017 |
| DIIN | 0.4869 | 0.782 | 10000 | 220.748 s | 14 | 字向量+动态词向量 | DIIN | 2018 | |
| TF | 0.6537 | 0.699 | 2630.853 s | 10000 | 7.384 s | 44 | 动态词向量+位置向量 | TF | 2018 |
| RE2 | 0.5115 | 0.763 | 7374.503 s | 10000 | 15.281 s | 18 | 字向量+动态词向量 | RE2 | 2019 |
| GTF | 0.6438 | 0.698 | 23725.764 s | 10000 | 54.646 s | 40 | n-gram字向量+词向量+位置向量 | [GTF] |