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