NLI Introduction
Challenges
- 词语匹配的多元性（同义词、一词多义）lexical gap、lexical variant；
- 短语匹配的结构性、多粒度计算语义；
- 文本匹配的层次性；
- 利用不相似部分的信息；
- 自然语言的基础现象：Variability of Semantic expression
- 是语言歧义的对偶问题；
- 语义表达的多变性、语言结构的多样性；
Approachs
models on NLI(Natural Language Infernce), concluded as: sentence-encoding-based vs. interaction-based
- 基于encoding的基本框架：
- input encoding —>concat vector —>vector & element-wise difference/product—> prediction
- Pros:
- 1、可预处理；2、易于建立索引；
- Cons:
- 1、失去语义焦点，易语义偏移；2、词上下文重要性难以衡量；
- 基于交互的基本框架：
- input encoding —> inference(interaction) —> composition —> prediction
- Pros:
- 1、把握语义焦点；2、可以对上下文重要性进行合理的建模；
- Cons:
- 1、信息损失（主要由交互带来）；
Measure of Similarity
Models of Sentence Encoding Based
- BiLSTM-Max
- NSE
- Deep Gated Attn. BiLSTM
- Residual Stacked Encoder
- Reinforced Self-Attention Network
- Distance-based Self-Attention Network
- Hierarchical BiLSTM with Max Pooling
- Dynamic Self-Attention Model
Models of Interaction Based
- Decomposable Attention
- ESIM
- KIM
- Densely Interactive Inference Network (DIIN)
- BIMPM
- Multi-Way Attention
- DR-BiLSTM
- CAFE
- Densely-Connected Recurrent and Co-Attentive Network
- DMAN
- SLRC
- AFN