Systems and methods for multi-user multi-lingual communications
First Claim
1. A method implemented by a data processing apparatus, the method comprising:
- selecting a mixture of old training data and new training data, the old training data comprising an old text message for which a correct translation to a different language is known, the new training data comprising a new text message for which a correct translation to the different language is not known;
sending a plurality of respective requests at different times to a client device of a user, the requests comprising (i) a respective request for the user to translate at least one of the old training data and the new training data and (ii) a respective incentive for the translation;
after sending a particular request, receiving a translation from the client device for the old training data of the particular request;
comparing the received translation with the correct translation for the old training data;
determining an accuracy of the received translation based on the comparison;
detecting collusion between the user and a second user by identifying a pre-existing relationship between the user and the second user; and
updating a confidence score for the user based on the translation using item response theory to identify a deviation from a norm in user translation accuracy, the confidence score representing a likelihood that the user will provide an accurate translation of a text message to the different language at a later time.
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Accused Products
Abstract
Various embodiments described herein facilitate multi-lingual communications. The systems and methods of some embodiments enable multi-lingual communications through different modes of communication including, for example, Internet-based chat, e-mail, text-based mobile phone communications, postings to online forums, postings to online social media services, and the like. Certain embodiments implement communication systems and methods that translate text between two or more languages. Users of the systems and methods may be incentivized to submit corrections for inaccurate or erroneous translations, and may receive a reward for these submissions. Systems and methods for assessing the accuracy of translations are described.
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Citations
21 Claims
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1. A method implemented by a data processing apparatus, the method comprising:
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selecting a mixture of old training data and new training data, the old training data comprising an old text message for which a correct translation to a different language is known, the new training data comprising a new text message for which a correct translation to the different language is not known; sending a plurality of respective requests at different times to a client device of a user, the requests comprising (i) a respective request for the user to translate at least one of the old training data and the new training data and (ii) a respective incentive for the translation; after sending a particular request, receiving a translation from the client device for the old training data of the particular request; comparing the received translation with the correct translation for the old training data; determining an accuracy of the received translation based on the comparison; detecting collusion between the user and a second user by identifying a pre-existing relationship between the user and the second user; and updating a confidence score for the user based on the translation using item response theory to identify a deviation from a norm in user translation accuracy, the confidence score representing a likelihood that the user will provide an accurate translation of a text message to the different language at a later time. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A system comprising:
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a computer readable medium having instructions stored thereon; and a data processing apparatus configured to execute the instructions to perform operations comprising; selecting a mixture of old training data and new training data, the old training data comprising an old text message for which a correct translation to a different language is known, the new training data comprising a new text message for which a correct translation to the different language is not known; sending a plurality of respective requests at different times to a client device of a user, the requests comprising (i) a respective request for the user to translate at least one of the old training data and the new training data and (ii) a respective incentive for the translation; after sending a particular request, receiving a translation from the client device for the old training data of the particular request; comparing the received translation with the correct translation for the old training data; determining an accuracy of the received translation based on the comparison; detecting collusion between the user and a second user by identifying a pre-existing relationship between the user and the second user; and updating a confidence score for the user based on the translation using item response theory to identify a deviation from a norm in user translation accuracy, the confidence score representing a likelihood that the user will provide an accurate translation of a text message to the different language at a later time. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A computer program product stored in one or more non-transitory storage media for controlling a processing mode of a data processing apparatus, the computer program product being executable by the data processing apparatus to cause the data processing apparatus to perform operations comprising:
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selecting a mixture of old training data and new training data, the old training data comprising an old text message for which a correct translation to a different language is known, the new training data comprising a new text message for which a correct translation to the different language is not known; sending a plurality of respective requests at different times to a client device of a user, the requests comprising (i) a respective request for the user to translate at least one of the old training data and the new training data and (ii) a respective incentive for the translation; after sending a particular request, receiving a translation from the client device for the old training data of the particular request; comparing the received translation with the correct translation for the old training data; determining an accuracy of the received translation based on the comparison; detecting collusion between the user and a second user by identifying a pre-existing relationship between the user and the second user; and updating a confidence score for the user based on the translation using item response theory to identify a deviation from a norm in user translation accuracy, the confidence score representing a likelihood that the user will provide an accurate translation of a text message to the different language at a later time. - View Dependent Claims (16, 17, 18, 19, 20, 21)
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Specification