Naver Corporation

Quentin Grail

Julien Perez

Eric Jacques Guy Gaussier

A method of using a first neural network includes: by the first neural network, receiving a text; by the first neural network, receiving a question concerning the text; and by the first neural network, determining an answer to the question using the text, where the first neural network is trained to answer the question about the text adversarially by a second neural network that is trained to maximize a likelihood of failure of the first neural network to correctly answer questions.

Training of machine reading and comprehension systems

A question answering system includes: a first encoder module configured to receive a question, the question including a first plurality of words, and encode the question into a first vector representation; a second encoder module configured to encode a document into a second vector representation, the document including a second plurality of words; a first reading module configured to generate a third vector representation based on the first and second vector representations; a first reformulation module configured to generate a first reformulated vector representation based on the first vector representation; a second reading module configured to generate a fifth vector representation based on the second vector representation and the first reformulated vector representation; a second reformulation module configured to generate a second reformulated vector representation based on first reformulated vector representation; and an answer module configured to determine an answer to the question based on the second reformulated vector representation.

Latent question reformulation and information accumulation for multi-hop machine reading

There is disclosed a computer implemented method that includes accessing a dataset having (1) a first set of questions including at least one pair of relational questions that correspond respectively with a pair of binary answers and (2) a second set of questions including at least another pair of relational questions that correspond respectively with a binary answer and a scalar answer. A question answering network is used to compute both a relational loss for the at least one pair of relational questions, and a relational loss for the at least another pair of relational questions. Both the relational loss for the at least one pair of relational questions and the relational loss for the at least another pair of relational questions are optimized, and a neural network model is trained with the optimized relational losses.

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Meylan, France

Quentin Grail