Amazon Technologies, Inc.

Salesforce Com, Inc.

Yifan Gao

Chu Hong Hoi

Bing Xiang

Ramesh M Nallapati

Shafiq Rayhan Joty

Zhiguo Wang

Cicero Nogueira Dos Santos

Patrick Ng

Chien-Sheng Wu

Andrew Oliver Arnold

Henghui Zhu

Dejiao Zhang

Zheng Li

Bing Yin

Feng Nan

Yiwei Song

Qingyu Yin

Techniques for predicting an answer to a question using a machine learning model are described. In some examples, the model predicts one or more answers to the question by: predicting at least two answers to the question using a first component of the question-answer model from a set of passages, generating, using a second component of the question-answer model, at least one question for each of the predicted at least two answers, and performing roundtrip predictions until each generated question only has one answer.

Questions disambiguation using generative evidence fusion and round-trip prediction

Systems and methods for multilingual keyword generation for low-resource language passages are disclosed. For example, high-resource language passages describing items in a catalog or other such passages may include numerous keywords for classifying and searching for the item while a low-resource language description of the item may not include the same rich keyword environment due to lower usage and traffic in the low-resource language. The systems and methods herein provide for leveraging the high-resource language through a multilingual natural language processing algorithm to identify similar passages across language barriers and identify keywords in the high-resource language for input into a decoder to generate keywords in the low-resource language.

Multilingual keyword generation

Embodiments described herein provide systems and methods for an Explicit Memory Tracker (EMT) that tracks each rule sentence to perform decision making and to generate follow-up clarifying questions. Specifically, the EMT first segments the regulation text into several rule sentences and allocates the segmented rule sentences into memory modules, and then feeds information regarding the user scenario and dialogue history into the EMT sequentially to update each memory module separately. At each dialogue turn, the EMT makes a decision among based on current memory status of the memory modules whether further clarification is needed to come up with an answer to a user question. The EMT determines that further clarification is needed by identifying an underspecified rule sentence span by modulating token-level span distributions with sentence-level selection scores. The EMT extracts the underspecified rule sentence span and rephrases the underspecified rule sentence span to generate a follow-up question.

Systems and methods for explicit memory tracker with coarse-to-fine reasoning in conversational machine reading

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Hong Kong, China

Yifan Gao