Light_SimMatch
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
文本匹配模型
- 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】
实验结果
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] |