Personalized natural language processing system

1. A personalized natural language processing system comprising:

at least one processor, communicatively coupled to non-volatile memory storing a natural language processing (NLP) model personalized for use by multiple users and instructions that, when executed by the processor, cause the processor to:

receive or retrieve a plurality of sets of raw text data from a plurality of users, respectively;

tokenize the plurality of sets of raw text data to generate a plurality of sets of tokenized text data for the plurality of users, respectively, each set of tokenized text data including a sequence of tokens corresponding to the raw text data, the tokens at least identifying distinct words or portions of words in the raw text data;

append predetermined user-specific tokens to the plurality of sets of tokenized text data from the plurality of users, respectively, to generate a plurality of user-specific token sets, each predetermined user-specific token corresponding to one user of the plurality of users;

process the plurality of user-specific token sets using the NLP model in accordance with the appended predetermined user-specific tokens to predict a personalized classification for each of the plurality of user-specific token sets; and

output the personalized classifications of the plurality of user-specific token sets, wherein the NLP model is trained using a training data set comprising multiple tuples of training data, each tuple including user identifier data that identifies a particular user, raw text data from the particular user, and ground truth classification data, the trained NLP model being configured to perform personalized text classification and/or text prediction tasks for each user of the multiple users, and during processing of the plurality of user-specific token sets, embeddings are produced for each token in each set of the plurality of user-specific token sets, and attention weights are computed between each token in each user-specific token set using the embeddings for each token.

2. The personalized natural language processing system of claim 1 , wherein the NLP model is a text classification model; and the personalized classifications are personalized text classifications for each of the plurality of users.

3. The personalized natural language processing system of claim 1 , wherein the NLP model is a text prediction model; and the personalized classifications are personalized text predictions for each of the plurality of users.

4. The personalized natural language processing system of claim 1 , wherein the predetermined user-specific tokens include at least one of consecutive numbers, usernames, random sequences of digits, random sequences of tokens with non-alphanumeric characters, or random sequences of all available tokens in a tokenizer vocabulary.

5. The personalized natural language processing system of claim 1 , wherein the training of the NLP model includes minimizing cross-entropy loss for classification.

6. The personalized natural language processing system of claim 1 , wherein the predetermined user-specific tokens are appended to the beginning and the end of each set of tokenized text data.

7. The personalized natural language processing system of claim 1 , wherein lengths of the predetermined user-specific tokens do not exceed a predetermined number of tokens.

8. The personalized natural language processing system of claim 1 , wherein the NLP model is a transformer sequence classifier, transformer sequence-to-sequence model, or long short-term memory (LSTM) recurrent neural network (RNN) classifier.

9. The personalized natural language processing system of claim 1 , wherein the NLP model is a transformer sequence classifier, and user embedding parameters of the predetermined user-specific tokens are tied to embedding parameters of the transformer sequence classifier.

10. A personalized natural language processing method, comprising:

receiving or retrieve a plurality of sets of raw text data from a plurality of users, respectively;

tokenizing the plurality of sets of raw text data to generate a plurality of sets of tokenized text data for the plurality of users, respectively, each set of tokenized text data including a sequence of tokens corresponding to the raw text data, the tokens at least identifying distinct words or portions of words in the raw text data;

appending predetermined user-specific tokens to the plurality of sets of tokenized text data from the plurality of users, respectively, to generate a plurality of user-specific token sets, each predetermined user-specific token corresponding to one user of the plurality of users;

processing the plurality of user-specific token sets sing a natural language processing (NLP) model in accordance with the appended predetermined user-specific tokens to predict a personalized classification for each of the plurality of user-specific token sets; and

outputting the personalized classifications of the plurality of user-specific token sets, wherein the NLP model is trained using a training data set comprising multiple tuples of training data, each tuple including user identifier data that identifies a particular user, raw text data from the particular user, and ground truth classification data, the trained NLP model being configured to perform personalized text classification and/or text prediction tasks for each user of the multiple users, and during processing of the plurality of user-specific token sets, embeddings are produced for each token in each set of the plurality of user-specific token sets, and attention weights are computed between each token in each user-specific token set using the embeddings for each token.

11. The personalized natural language processing method of claim 10 , wherein the NLP model is a text classification model; and the personalized classifications are personalized text classifications for each of the plurality of users.

12. The personalized natural language processing method of claim 10 , wherein the NLP model is a text prediction model; and the personalized classifications are personalized text predictions for each of the plurality of users.

13. The personalized natural language processing method of claim 10 , wherein the predetermined user-specific tokens comprise one of consecutive numbers, usernames, random sequences of digits, random sequences of tokens with non-alphanumeric characters, or random sequences of all available tokens in a tokenizer vocabulary.

14. The personalized natural language processing method of claim 10 , wherein the training of the NLP model includes minimizing cross-entropy loss for classification.

15. The personalized natural language processing method of claim 10 , wherein the predetermined user-specific tokens are appended to a beginning and an end of each set of tokenized text data.

16. The personalized natural language processing method of claim 10 , wherein the NLP model is a transformer sequence classifier, transformer sequence-to-sequence model, or long short-term memory (LSTM) recurrent neural network (RNN) classifier.

17. The personalized natural language processing method of claim 10 , wherein the NLP model is a transformer sequence classifier, and user embedding parameters of the predetermined user-specific tokens are tied to embedding parameters of the transformer sequence classifier.

18. A personalized natural language processing system comprising:

at least one processor, communicatively coupled to non-volatile memory storing a sentiment analysis model and instructions that, when executed by the processor, cause the processor to:

receive or retrieve a plurality of sets of utterances from a plurality of users, respectively;

tokenize the plurality of sets of utterances to generate a plurality of sets of tokenized text data for the plurality of users, respectively, each set of tokenized text data including a sequence of tokens corresponding to the raw text data, the tokens at least identifying distinct words or portions of words in the raw text data;

append predetermined user-specific tokens to the plurality of sets of tokenized text data from the plurality of users, respectively, to generate a plurality of user-specific token sets, each predetermined user-specific token corresponding to one user of the plurality of users;

process the plurality of user-specific token sets using the sentiment analysis model in accordance with the appended predetermined user-specific tokens to predict a personalized classification for each of the plurality of user-specific token sets; and

output the personalized classifications of the plurality of user-specific token sets, wherein the sentiment analysis model is trained using a training data set comprising multiple tuples of training data, each tuple including user identifier data that identifies a particular user, raw text data from the particular user, and ground truth classification data, the trained NLP model being configured to perform personalized text classification and/or text prediction tasks for each user of the multiple users, during processing of the plurality of user-specific token sets, embeddings are produced for each token in each set of the plurality of user-specific token sets, and attention weights are computed between each token in each user-specific token set using the embeddings for each token, and the personalized classifications include a plurality of sentiment labels including at least a positive sentiment, a neutral sentiment, and a negative sentiment.