TRAINING OF MACHINE READING AND COMPREHENSION SYSTEMS
First Claim
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1. A computer-executable method for training a machine reading network, comprising:
- by a first neural network, analyzing a first text corpus extracted from a training dataset comprising a plurality of text corpuses;
by a second neural network, obfuscating words of a passage of the first text corpus in order to train the first neural network, the obfuscation producing a first corrupted text corpus;
by the second neural network, generating a first question about content of the first corrupted text corpus;
by the first neural network, analyzing the first corrupted text corpus and determining a first answer to the first question based on the first corrupted text corpus;
by the second neural network, analyzing the first answer of the first neural network and generating a first performance index for the first neural network based on the first answer, the first performance index representing a first answering performance of the first neural network given the obfuscation of the first corrupted text corpus;
by the second neural network storing the first performance index in memory;
by the second neural network, generating a second corrupted text corpus by obfuscating words of a second text corpus extracted from the training dataset, the obfuscation being based on the first performance index of the first neural network;
by the second neural network, generating a second question about content of the second corrupted text corpus;
by the first neural network, analyzing the second corrupted text corpus and determining a second answer to the second question based on the second corrupted text corpus;
by the second neural network, analyzing the second answer of the first neural network and generating a second performance index for the first neural network based on the second answer, the second performance index representing a second answering performance of the first neural network given the obfuscation of the second corrupted text corpus; and
by the second neural network storing the second performance index in memory.
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Abstract
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.
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Citations
20 Claims
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1. A computer-executable method for training a machine reading network, comprising:
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by a first neural network, analyzing a first text corpus extracted from a training dataset comprising a plurality of text corpuses; by a second neural network, obfuscating words of a passage of the first text corpus in order to train the first neural network, the obfuscation producing a first corrupted text corpus; by the second neural network, generating a first question about content of the first corrupted text corpus; by the first neural network, analyzing the first corrupted text corpus and determining a first answer to the first question based on the first corrupted text corpus; by the second neural network, analyzing the first answer of the first neural network and generating a first performance index for the first neural network based on the first answer, the first performance index representing a first answering performance of the first neural network given the obfuscation of the first corrupted text corpus; by the second neural network storing the first performance index in memory; by the second neural network, generating a second corrupted text corpus by obfuscating words of a second text corpus extracted from the training dataset, the obfuscation being based on the first performance index of the first neural network; by the second neural network, generating a second question about content of the second corrupted text corpus; by the first neural network, analyzing the second corrupted text corpus and determining a second answer to the second question based on the second corrupted text corpus; by the second neural network, analyzing the second answer of the first neural network and generating a second performance index for the first neural network based on the second answer, the second performance index representing a second answering performance of the first neural network given the obfuscation of the second corrupted text corpus; and by the second neural network storing the second performance index in memory. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A computer-executable method for training a machine reading network, comprising:
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training a first neural network to analyze text corpuses and to answer questions regarding passages of the text corpuses; and training a second neural network to generate a first corrupted text corpus by obfuscating words of a first passage of a first text corpus in order to minimize a probability of the first neural network correctly answering a first question regarding the first corrupted text corpus, wherein the training the second neural network includes training the second neural network to; analyze a first answer of the first neural network to the first question; assess a bias in the first answer caused by the obfuscation of the words of the passage of the first text corpus; and based on the bias in the first answer of the first neural network, generate a second corrupted text corpus by obfuscating words of a second passage of a second text corpus. - View Dependent Claims (10, 11, 12, 13, 14)
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15. A method of using a first neural network, comprising;
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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, wherein 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. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification