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Nowadays, neural network models achieve state-of-the-art results in manyareas as computer vision or speech processing. For sequential data, especiallyfor Natural Language Processing (NLP) tasks, Recurrent Neural Networks (RNNs)and their extensions, the Long Short Term Memory (LSTM) network and the GatedRecurrent Unit (GRU), are among the most used models, having a "term-to-term"sequence processing. However, if many works create extensions and improvementsof the RNN, few have focused on developing other ways for sequential dataprocessing with neural networks in a "term-to-term" way. This paper proposesthe original Hidden Neural Markov Chain (HNMC) framework, a new family ofsequential neural models. They are not based on the RNN but on the HiddenMarkov Model (HMM), a probabilistic graphical model. This neural extension ispossible thanks to the recent Entropic Forward-Backward algorithm for HMMrestoration. We propose three different models: the classic HNMC, the HNMC2,and the HNMC-CN. After describing our models' whole construction, we comparethem with classic RNN and Bidirectional RNN (BiRNN) models for some sequencelabeling tasks: Chunking, Part-Of-Speech Tagging, and Named Entity Recognition.For every experiment, whatever the architecture or the embedding method used,one of our proposed models has the best results. It shows this new neuralsequential framework's potential, which can open the way to new models, andmight eventually compete with the prevalent BiLSTM and BiGRU.

Introducing the Hidden Neural Markov Chain framework