| Paper | Conference | Remarks |
|---|---|---|
| Convolutional Neural Networks for Sentence Classification | EMNLP 2014 | Show that a simple CNN with little hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks. |
| A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification | Arxiv 2015 | Conduct a sensitivity analysis of one-layer CNNs to explore the effect of architecture components on model performance, and to distinguish between important and comparatively inconsequential design decisions for sentence classification. |
| Hierarchical Attention Networks for Document Classification | NAACL 2016 | 1. Propose a hierarchical attention network for document classification. 2. It has a hierarchical structure that mirrors the hierarchical structure of documents. 3. It has two levels of attention mechanisms applied at the wordand sentence-level, enabling it to attend differentially to more and less important content when constructing the document representation. 4. Unclear about the interpretation of learned attention word and sentence. |
| Universal Language Model Fine-tuning for Text Classification | ACL 2018 | 1. Propose Universal Language Model Fine-tuning (ULMFiT), an effective transfer learning method that can be applied to any task in NLP, and introduce techniques that are key for fine-tuning a language model. 2. Significantly outperforms the state-of-the-art on six text classification tasks |