International Business Machines Corporation

Lin Pan

Saloni Potdar

Haode Qi

Abhishek Shah

Mary Swift

Mihaela Ancuta Bornea

Radu Florian

Avirup Sil

Ladislav Kunc

James William Murdock, Iv

Christopher Munjal Nolan

Sara Rosenthal

Sai Prathyusha Peddi

Nisarga Markandaiah

Eun Ha

Chung-Wei Hang

Kazi Hasan

A system for classifying a language sample intent by receiving a language sample including a set of features, identifying language sample features, determining a tokenization score for the language sample according to the language sample features, eliminating duplicate features according to the tokenization score, determining a term frequency (tf) according to the identified features and the tokenization score, determining an inverse document frequency (idf) according to the identified features and the tokenization score, and generating a term frequency-inverse document frequency (tf-idf) matrix for the identified features.

Intent classification using non-correlated features

A method, computer system, and computer program product for multi-lingual chatlog training are provided. The embodiment may include receiving, by a processor, a plurality of data related to conversational data in multiple languages. The embodiment may also include assigning an intent label to each conversational data. The embodiment may further include assigning a language label to each conversational data. The embodiment may also include paring the plurality of the data related to the conversational data according to the intent label and the language label. The embodiment may further include training a machine learning model using a multi-lingual and multi-intent conversational data pairing. The embodiment may also include training the machine learning model using a single language and multi-intent conversational data paring.

Conversational AI with multi-lingual human chatlogs

A method includes receiving, by a question-answer system, a question in a first language and the question in a second language and predicting, by the question-answer system, a first answer to the question in the first language and a second answer to the question in the second language. The method also includes generating, by the question-answer system, a first vector representing the question in the first language and a second vector representing the question in the second language and adjusting the question-answer system based on the first and second answers and the first and second vectors such that when the question-answer system subsequently generates a third vector representing the question in the first language and a fourth vector representing the question in the second language, a distance between the third and fourth vectors is less than a distance between the first and second vectors.

Enhancing multi-lingual embeddings for cross-lingual question-answer system

Systems and methods for generating answers to questions. One method includes receiving a question having question terms; identifying candidate answers to the question having answer terms; searching data sources to determine passages including either a question term or an answer term in the candidate answer; scoring the passages for candidate answers using a scoring mechanism, the scoring mechanism computing a first degree of relevance of the passage to the question terms, computing a second degree of relevance of the passage to the answer terms of one of the candidate answers, and determining a score for the passage by combining the first degree of relevance and the second degree of relevance; ranking candidate answers to the question based on the scores associated with the scoring each of the passages for each of the candidate answers; and providing an answer to the question based on ranking of the candidate answers.

Supporting evidence retrieval for complex answers

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Armonk, NY, United States of America

Lin Pan