METHODS AND SYSTEMS TO TRAIN CLASSIFICATION MODELS TO CLASSIFY CONVERSATIONS
First Claim
1. A method for training a conversation classification model, said method comprising:
- receiving, by a transceiver, a first set of conversations corresponding to a source domain and a second set of conversations corresponding to a target domain, wherein each conversation in said first set of conversations has an associated predetermined tag, and wherein each conversation in the first set of conversations and the second set of conversations corresponds to an audio conversation;
generating, by one or more processors, a transcript for each conversation in the first set of conversations and the second set of conversations based on a speech to text conversion technique;
extracting, by the one or more processors, one or more features from the transcript of each of said first set of conversations and said second set of conversations;
assigning, by the one or more processors, a first weight to each conversation in said first set of conversations based on at least a similarity between content of said first set of conversations and content of said second set of conversations, wherein the similarity of the content is determined based on the one or more features;
assigning, by the one or more processors, a second weight to each of said one or more features associated said first set of conversations based on a similarity between said one or more features extracted from the transcript of said first set of conversations and said one or more features extracted from the transcript of said second set of conversations; and
training, by the one or more processors, said conversation classification model based on at least said first weight and said second weight, wherein said conversation classification model is capable of assigning said predetermined tag to said second set of conversations.
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Abstract
Methods and systems for training a conversation-classification model are disclosed. A first set of conversations in a source domain and a second set of conversation in a target domain are received. Each of the first set of conversations has an associated predetermined tag. One or more features are extracted from the first set of conversations and from the second set of conversations. Based on the similarity of content in the first set of conversations and the second set of conversations, a first weight is assigned to each conversation of the first set of conversations. Further, a second weight is assigned to the one or more features of the first set of conversations based on the similarity of the one or more features of the first set of conversations and of the second set of conversations. A conversation-classification model is trained based on the first weight and the second weight.
15 Citations
16 Claims
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1. A method for training a conversation classification model, said method comprising:
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receiving, by a transceiver, a first set of conversations corresponding to a source domain and a second set of conversations corresponding to a target domain, wherein each conversation in said first set of conversations has an associated predetermined tag, and wherein each conversation in the first set of conversations and the second set of conversations corresponds to an audio conversation; generating, by one or more processors, a transcript for each conversation in the first set of conversations and the second set of conversations based on a speech to text conversion technique; extracting, by the one or more processors, one or more features from the transcript of each of said first set of conversations and said second set of conversations; assigning, by the one or more processors, a first weight to each conversation in said first set of conversations based on at least a similarity between content of said first set of conversations and content of said second set of conversations, wherein the similarity of the content is determined based on the one or more features; assigning, by the one or more processors, a second weight to each of said one or more features associated said first set of conversations based on a similarity between said one or more features extracted from the transcript of said first set of conversations and said one or more features extracted from the transcript of said second set of conversations; and training, by the one or more processors, said conversation classification model based on at least said first weight and said second weight, wherein said conversation classification model is capable of assigning said predetermined tag to said second set of conversations. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A system for training a conversation classification model, said system comprising:
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a transceiver configured to receive a first set of conversations corresponding to a source domain and a second set of conversations corresponding to a target domain, wherein each conversation in said first set of conversations has an associated predetermined tag, and wherein each conversation in the first set of conversations and the second set of conversations corresponds to an audio conversation; and one or more processors configured to; generate, by one or more processors, a transcript for each conversation in the first set of conversations and the second set of conversations based on a speech to text conversion technique; extract one or more features from the transcript of each of said first set of conversations and said second set of conversations, assign a first weight to each conversation in said first set of conversations based on at least a similarity between content of said first set of conversations and content of said second set of conversations, wherein the similarity of the content is determined based on the one or more features, assign a second weight to each of said one or more features associated said first set of conversations based on a similarity between said one or more features extracted from the transcript of said first set of conversations and said one or more features extracted from the transcript of said second set of conversations, and train said conversation classification model based on at least said first weight and said second weight, wherein said conversation classification model is capable of assigning said predetermined tag to said second set of conversations. - View Dependent Claims (10, 11, 12, 13, 14, 15)
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16. A computer program product for use with a computing device, the computer program product comprising a non-transitory computer readable medium, the non-transitory computer readable medium stores a computer program code for training a conversation classification model, the computer program code is executable by one or more processors in the computing device to:
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receive a first set of conversations corresponding to a source domain and a second set of conversations corresponding to a target domain, wherein each conversation in said first set of conversations has an associated predetermined tag, and wherein each conversation in the first set of conversations and the second set of conversations corresponds to an audio conversation; generate, by one or more processors, a transcript for each conversation in the first set of conversations and the second set of conversations based on a speech to text conversion technique; extract one or more features from the transcript of each of said first set of conversations and said second set of conversations; assign a first weight to each conversation in said first set of conversations based on at least a similarity between content of said first set of conversations and content of said second set of conversations, wherein the similarity of the content is determined based on the one or more features; assign a second weight to each of said one or more features associated said first set of conversations based on a similarity between said one or more features extracted from the transcript of said first set of conversations and said one or more features extracted from the transcript of said second set of conversations; and train said conversation classification model based on at least said first weight and said second weight, wherein said conversation classification model is capable of assigning said predetermined tag to said second set of conversations.
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Specification