Abstract: A method and apparatus comprising computer code configured to cause a processor or processors to receive a text comprising a plurality of sentences, by a machine learning model, extract a nonverbal message from one of the sentences and add an annotation to the text, the annotation indicating the nonverbal message, and output a version of the text including the annotation.

Abstract: The present disclosure describes various methods, systems, and storage medium for training a language model to obtain an iterative Nash policy optimized (INPO) language model. The method includes initializing a first language model by a reference language model; for N-th iteration with N starting from 1 to M, generating a plurality of responses using the N-th language model for each prompt in a plurality of prompts, and constructing a preference dataset using a preference oracle based on the plurality of responses for each prompt, wherein the preference dataset comprises a winning response and a losing response; training the N-th language model to obtain a (N+1)-th language model by minimizing a value of an INPO function for all preference dataset for the plurality of prompts, wherein the INPO function comprises an expectation term, a regularization term, and a modification term; and outputting the (M+1)-th language model.

Abstract: A method and apparatus that identifies one or more characters within a text; determines one or more informative sections within the text, the one or more informative sections providing information regarding a gender of the one or more characters within the text; selects a most informative section from the one or more informative sections; extracts unlabeled instances corresponding to the gender of the one or more characters from the most informative section; iteratively trains a multi-task model using unlabeled corpora, the multi-task model performing both speaker identification and gender identification; and labels the gender of the one or more characters based on the extracted unlabeled instances and the multi-task model.

Abstract: A method and apparatus that identifies one or more characters within a text; determines one or more informative sections within the text, the one or more informative sections providing information regarding a gender of the one or more characters within the text; selects a most informative section from the one or more informative sections; extracts unlabeled instances corresponding to the gender of the one or more characters from the most informative section; iteratively trains a multi-task model using unlabeled corpora, the multi-task model performing both speaker identification and gender identification; and labels the gender of the one or more characters based on the extracted unlabeled instances and the multi-task model.

Abstract: A method, computer program, and computer system is provided for identifying a speaker in at text based work. Labeled and unlabeled instances corresponding to one or more speakers are extracted. Pseudo-labels are inferred for the extracted unlabeled instances based on the labeled instances. One or more of the unlabeled instances are labeled based on the inferred pseudo-labels.

Abstract: A method and apparatus comprising computer code configured to cause a processor or processors to receive a text comprising a plurality of sentences, by a machine learning model, extract a nonverbal message from one of the sentences and add an annotation to the text, the annotation indicating the nonverbal message, and output a version of the text including the annotation.

Abstract: There is included a method and apparatus comprising computer code configured to cause a processor or processors to perform generating one or more aligned inventories, wherein the one or more aligned inventories are generated using one or more word sense inventories, obtaining a word in a context sentence, determining one or more semantic equivalence scores indicating semantic similarity between the word in the context sentence and each of one or more associated glosses in the one or more aligned inventories using a semantic equivalence recognizer model, and predicting a correct sense of the word in the context sentence based on the determined one or more semantic equivalence scores.

Abstract: A method and apparatus that identifies one or more characters within a text; determines one or more informative sections within the text, the one or more informative sections providing information regarding a gender of the one or more characters within the text; selects a most informative section from the one or more informative sections; extracts unlabeled instances corresponding to the gender of the one or more characters from the most informative section; iteratively trains a multi-task model using unlabeled corpora, the multi-task model performing both speaker identification and gender identification; and labels the gender of the one or more characters based on the extracted unlabeled instances and the multi-task model.

Abstract: A method including: receiving an input comprising natural language texts; selecting, via a knowledge selector, one of a plurality of knowledge categories from an external memory based on a context of the input; retrieving one or more helpful knowledge pieces from the selected knowledge category; augmenting the input using the one or more helpful knowledge pieces; feeding the augmented input into a text-to-text model; and generating an output answer based on the text-to-text model.

Abstract: A method, computer program, and computer system is provided for identifying a speaker in at text based work. Labeled and unlabeled instances corresponding to one or more speakers are extracted. Pseudo-labels are inferred for the extracted unlabeled instances based on the labeled instances. One or more of the unlabeled instances are labeled based on the inferred pseudo-labels.

Abstract: A method, computer program, and computer system is provided for identifying a speaker in at text based work. Labeled and unlabeled instances corresponding to one or more speakers are extracted. Pseudo-labels are inferred for the extracted unlabeled instances based on the labeled instances. One or more of the unlabeled instances are labeled based on the inferred pseudo-labels.

Abstract: There is included a method and apparatus comprising computer code configured to cause a processor or processors to perform generating one or more aligned inventories, wherein the one or more aligned inventories are generated using one or more word sense inventories, obtaining a word in a context sentence, determining one or more semantic equivalence scores indicating semantic similarity between the word in the context sentence and each of one or more associated glosses in the one or more aligned inventories using a semantic equivalence recognizer model, and predicting a correct sense of the word in the context sentence based on the determined one or more semantic equivalence scores.

Abstract: A method, computer program, and computer system is provided for identifying a speaker in at text based work. Labeled and unlabeled instances corresponding to one or more speakers are extracted. Pseudo-labels are inferred for the extracted unlabeled instances based on the labeled instances. One or more of the unlabeled instances are labeled based on the inferred pseudo-labels.