A **CNN BiLSTM** is a hybrid bidirectional [LSTM](https://paperswithcode.com/method/lstm) and CNN architecture. In the original formulation applied to named entity recognition, it learns both character-level and word-level features. The CNN component is used to induce the character-level features. For each word the model employs a [convolution](https://paperswithcode.com/method/convolution) and a [max pooling](https://paperswithcode.com/method/max-pooling) layer to extract a new feature vector from the per-character feature vectors such as character embeddings and (optionally) character type.

**VIME **, or **Value Imputation and Mask Estimation**, is a self- and semi-supervised learning framework for tabular data. It consists of a pretext task of estimating mask vectors from corrupted tabular data in addition to the reconstruction pretext task for self-supervised learning.

VIME

VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain

CNN BiLSTM

Named Entity Recognition with Bidirectional LSTM-CNNs

**Enhanced Sequential Inference Model** or **ESIM** is a sequential NLI model proposed in [Enhanced LSTM for Natural Language Inference](https://www.aclweb.org/anthology/P17-1152) paper.

Source	Named Entity Recognition with Bidirectional LSTM-CNNs
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com