Methods and systems to train classification models to classify conversations
First Claim
1. A method for training a conversation classification model, the 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 the first set of conversations has one or more predetermined tags,wherein at least one of the one or more predetermined tags corresponds to a status of the first set of conversations,wherein the source domain corresponds to a first technical or business field for which the one or more predetermined tags are associated and the target domain correspond to a second technical or business field, different from the first technical or business field, for which tags are not associated, andwherein each conversation in the first set of conversations and each conversation in the second set of conversations comprises an audio conversation;
generating, by one or more processors, a transcript for each conversation in the first set of conversations and a transcript for each conversation in 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 the first set of conversations and the second set of conversations;
assigning, by the one or more processors, a first weight to each conversation in the first set of conversations based on at least a similarity between content of the first set of conversations and content of the second set of conversations, wherein the similarity of the content is determined based on the one or more features extracted from the transcripts of the first set of conversations and the second set of conversations, and based on a ratio defined as;
6 Assignments
0 Petitions
Accused Products
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.
29 Citations
16 Claims
-
1. A method for training a conversation classification model, the 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 the first set of conversations has one or more predetermined tags, wherein at least one of the one or more predetermined tags corresponds to a status of the first set of conversations, wherein the source domain corresponds to a first technical or business field for which the one or more predetermined tags are associated and the target domain correspond to a second technical or business field, different from the first technical or business field, for which tags are not associated, and wherein each conversation in the first set of conversations and each conversation in the second set of conversations comprises an audio conversation; generating, by one or more processors, a transcript for each conversation in the first set of conversations and a transcript for each conversation in 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 the first set of conversations and the second set of conversations; assigning, by the one or more processors, a first weight to each conversation in the first set of conversations based on at least a similarity between content of the first set of conversations and content of the second set of conversations, wherein the similarity of the content is determined based on the one or more features extracted from the transcripts of the first set of conversations and the second set of conversations, and based on a ratio defined as; - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
-
-
9. A system for training a conversation classification model, said system comprising:
-
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 the first set of conversations has one or more predetermined tags, wherein at least one of the one or more predetermined tags corresponds to a status of the first set of conversations, wherein the source domain corresponds to a first technical or business field for which the one or more predetermined tags are associated and the target domain correspond to a second technical or business field, different from the first technical or business field, for which tags are not associated, and wherein each conversation in the first set of conversations and each conversation in the second set of conversations comprises an audio conversation; and one or more processors configured to; generate 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 the first set of conversations and from the transcript of the second set of conversations, assign a first weight to each conversation in the first set of conversations based on at least a similarity between content of the first set of conversations and content of the second set of conversations, wherein the similarity of the content is determined based on the one or more features extracted from the transcript of the first set of conversations and from the transcript of the second set of conversations, and based on a ratio defined as; - View Dependent Claims (10, 11, 12, 13, 14, 15)
-
-
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 storing a computer program code for training a conversation classification model, the computer program code being executable by one or more processors in the computing device 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 the first set of conversations has one or more predetermined tags, wherein at least one of the one or more predetermined tags corresponds to a status of the first set of conversations, wherein the source domain corresponds to a first technical or business field for which the one or more predetermined tags are associated and the target domain correspond to a second technical or business field, different from the first technical or business field, for which tags are not associated, and wherein each conversation in the first set of conversations and each conversation in the second set of conversations comprises an audio conversation; generate a transcript for each conversation in the first set of conversations and each conversation in the second set of conversations based on a speech-to-text conversion technique; extract one or more features from the transcripts of each of the first set of conversations and the second set of conversations; assign a first weight to each conversation in the first set of conversations based on at least a similarity between content of the first set of conversations and content of the second set of conversations, wherein the similarity of the content is determined based on the extracted one or more features of each of the first set of conversations and the second set of conversations, and based on a ratio defined as;
-
Specification