Disclosed are systems, methods, circuits and associated computer executable code for deep learning based natural language understanding, wherein training of one or more neural networks, includes: producing character strings inputs ‘noise’ on a per-character basis, and introducing the produced ‘noise’ into machine training character strings inputs fed to a ‘word tokenization and spelling correction language-model’, to generate spell corrected word sets outputs; feeding machine training word sets inputs, including one or more ‘right’ examples of correctly semantically-tagged word sets, to a ‘word semantics derivation model’, to generate semantically tagged sentences outputs. Upon models reaching a training ‘steady state’, the ‘word tokenization and spelling correction language-model’ is fed with input character strings representing ‘real’ linguistic user inputs, generating word sets outputs that are fed as inputs to the word semantics derivation model for generating semantically tagged sentences outputs.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A neural network based system for spell correction and tokenization of natural language, said system comprising: An artificial neural network architecture, to generate variable length ‘character level output streams’ for system fed variable length ‘character level input streams’; An auto-encoder for injecting random character level modifications to the variable length ‘character level input streams’, wherein the characters include a space-between-token character; and An unsupervised training mechanism for adjusting said neural network to learn correct variable length ‘character level output streams’, wherein correct variable length ‘character level output streams’ needs to be similar to respective original variable length ‘character level input streams’ prior to their random character level modifications. 2. The system according to claim 1 wherein the random character level modifications are selected from the group consisting of adding random characters, deleting characters, transposing characters and replacing characters. 3. The system according to claim 2 wherein said neural network is implemented using a sequence to sequence artificial neural network architecture, sequences of the variable length ‘character level input streams’ are mapped to a hidden state, and sequences of the variable length ‘character level output streams’ are generated from the hidden state. 4. The system according to claim 3 wherein the sequence to sequence artificial neural network architecture is implemented using a bidirectional long short-term memory (LSTM) input layer. 5. The system according to claim 4 wherein the variable length ‘character level input streams’ are Unicode character streams, and further comprising a UTF-8 encoder for applying UTF-8 encoding to the Unicode character streams prior to their inputting to said neural network. 6. The system according to claim 5 wherein said unsupervised training mechanism is further adapted for adjusting said neural network to learn a per-character embedding representation of the variable length ‘character level input streams’, in parallel to the learning of correct variable length ‘character level output streams’ 7. The system according to claim 2 further comprising a random modification selector for randomly selecting the character level modifications from the group. 8. The system according to claim 7, wherein said auto-encoder is further adapted for incrementing the frequency of injecting the random character level modifications to the variable length ‘character level input streams’, responsive to an increase in the level of similarity of the variable length ‘character level output streams’ to the respective original variable length ‘character level input streams’ prior to their random character level modifications. 9. The system according to claim 1, wherein at least some of the variable length ‘character level input streams’, fed to the system represent dialogs, and dialog metadata is at least partially utilized by said artificial neural network to generate the variable length ‘character level output streams’. 10. The system according to claim 9, wherein dialog metadata at least partially includes dialog state data. 11. A neural network based system for semantic role assignment of dialog utterances, said system comprising: An artificial recurrent neural network architecture, implemented using long short-term memory (LSTM) cells, to generate variable length ‘tagged tokens output streams’ for system fed variable length ‘dialog utterance input streams’; and A weakly supervised training mechanism for feeding to said artificial recurrent neural network, one or more variable length ‘dialog utterance input streams’ with their respective correctly-tagged variable length ‘tagged tokens output streams’, as initial input training data, and for adjusting said recurrent neural network to learn correct variable length ‘tagged tokens output streams’, by generating, and suggesting for system curator tagging correctness feedback—additional variable length ‘dialog utterance input streams’ with their respective variable length ‘tagged tokens output streams’ as tagged by said recurrent neural network—wherein correct tagging of the suggested additional variable length ‘dialog utterance input streams’ improves the capability of said recurrent neural network to refine the decision boundaries between correctly and incorrectly tagged inputs and to more correctly tag following system fed variable length ‘dialog utterance input streams’. 12. The system according to claim 11, wherein at least some of the variable length ‘dialog utterance input streams’, fed to the system represent dialogs, and dialog metadata is at least partially utilized by said artificial recurrent neural network to generate the variable length ‘tagged tokens output streams’. 13. The system according to claim 12, wherein dialog metadata at least partially includes dialog state data. 14. The system according to claim 11, wherein said weakly supervised training mechanism is further adapted to modify the variable length ‘tagged tokens output stream’ of a specific given incorrectly labeled variable length ‘dialog utterance input stream’, without retraining of the entire said recurrent neural network, by reiterating the variable length ‘dialog utterance input stream’ and applying gradient learning with a low learning rate across multiple training epochs. 15. The system according to claim 11, wherein said weakly supervised training mechanism is further adapted to self-improve while actively handling real end-user variable length ‘dialog utterance input streams’ by utilizing under-utilized Central Processing Unit (CPU) cycles of its hosting computer to run additional epochs of training. 16. The system according to claim 1, further comprising: An artificial recurrent neural network architecture, implemented using long short-term memory (LSTM) cells, to generate variable length ‘tagged tokens output streams’ for system fed variable length ‘dialog utterance input streams’; and A weakly supervised training mechanism for feeding to said artificial recurrent neural network, one or more variable length ‘dialog utterance input streams’ with their respective correctly-tagged variable length ‘tagged tokens output streams’, as initial input training data, and for adjusting said recurrent neural network to learn correct variable length ‘tagged tokens output streams’, by generating, and suggesting for system curator tagging correctness feedback—additional variable length ‘dialog utterance input streams’ with their respective variable length ‘tagged tokens output streams’ as tagged by said recurrent neural network—wherein correct tagging of the suggested additional variable length ‘dialog utterance input streams’ improves the capability of said recurrent neural network to refine the decision boundaries between correctly and incorrectly tagged inputs and to more correctly tag following system fed variable length ‘dialog utterance input streams’; and wherein variable length ‘character level output streams’ generated by said artificial neural network for variable length ‘character level input streams’, are fed as variable length ‘dialog utterance input streams’ to said artificial recurrent neural network. 17. A method for spell correction and tokenization of natural language, said method comprising: feeding variable length ‘character level input streams’ to an artificial neural network architecture, to generate variable length ‘character level output streams’; injecting random character level modifications to the variable length ‘character level input streams’, wherein the characters include a space-between-token character; and adjusting the neural network to learn correct variable length ‘character level output streams’, wherein correct variable length ‘character level output streams’ needs to be similar to respective original variable length ‘character level input streams’ prior to their random character level modifications. 18. The method according to claim 17 wherein the random character level modifications are selected from the group consisting of adding random characters, deleting characters, transposing characters and replacing characters. 19. A method for semantic role assignment of dialog utterances, said method comprising: feeding variable length ‘dialog utterance input streams’ to an artificial recurrent neural network architecture, to generate variable length ‘dialog utterance input streams’; feeding to the artificial recurrent neural network, one or more variable length ‘dialog utterance input streams’ with their respective correctly-tagged variable length ‘tagged tokens output streams’, as initial input training data; and adjusting the recurrent neural network to learn correct variable length ‘tagged tokens output streams’, by generating, and suggesting for system curator tagging correctness feedback—additional variable length ‘dialog utterance input streams’ with their respective variable length ‘tagged tokens output streams’ as tagged by the recurrent neural network—wherein correct tagging of the suggested additional variable length ‘dialog utterance input streams’ improves the capability of the recurrent neural network to refine the decision boundaries between correctly and incorrectly tagged inputs and to more correctly tag following fed variable length ‘dialog utterance input streams’. 20. The method according to claim 19, wherein the weakly supervised training mechanism is further adapted to further comprising modifying the variable length ‘tagged tokens output stream’ of a specific given incorrectly labeled variable length ‘dialog utterance input stream’, without retraining of the entire recurrent neural network, by reiterating the variable length ‘dialog utterance input stream’ and applying gradient learning with a low learning rate across multiple training epochs.