- 专利标题: Language-model-based data augmentation method for textual classification tasks with little data
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申请号: US16870917申请日: 2020-05-09
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公开(公告)号: US11526667B2公开(公告)日: 2022-12-13
- 发明人: Amir Kantor , Ateret Anaby Tavor , Boaz Carmeli , Esther Goldbraich , George Kour , Segev Shlomov , Naama Tepper , Naama Zwerdling
- 申请人: International Business Machines Corporation
- 申请人地址: US NY Armonk
- 专利权人: International Business Machines Corporation
- 当前专利权人: International Business Machines Corporation
- 当前专利权人地址: US NY Armonk
- 主分类号: G06F40/279
- IPC分类号: G06F40/279 ; G06N5/04 ; G06N20/00
摘要:
Embodiments of the present systems and methods may provide techniques for augmenting textual data that may be used for textual classification tasks. Embodiments of such techniques may provide the capability to synthesize labeled data to improve text classification tasks. Embodiments may be specifically useful when only a small amount of data is available, and provide improved performance in such cases. For example, in an embodiment, a method implemented in a computer system may comprise a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, and the method may comprise fine-tuning a language model using a training dataset, synthesizing a plurality of samples using the fine-tuned language model, filtering the plurality of synthesized samples, and generating an augmented training dataset comprising the training dataset and the filtered plurality of synthesized sentences.
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