In:
ACM Transactions on Asian and Low-Resource Language Information Processing, Association for Computing Machinery (ACM), Vol. 21, No. 4 ( 2022-07-31), p. 1-17
Abstract:
Emotion recognition in conversation is one of the essential tasks of natural language processing. However, this task’s annotation data is insufficient since such data is hard to collect and annotate. Meanwhile, there is large-scale data for conversational generation, and this data does not need annotation manually. But, whether the vector space between different datasets is similar will be a problem. Therefore, we utilize a same dataset to train the conversational generator and the classifier, and transfer knowledge between them. In particular, we propose an Emotion Recognition with Conversational Generation Transfer (ERCGT) framework to model the interaction among utterances by transfer learning. First, we train a conversational generator. In the second step, a transfer learning model is used to transfer the knowledge of generator to the emotion recognition model. Empirical studies illustrate the effectiveness of the proposed framework over several strong baselines on three benchmark emotion classification datasets.
Type of Medium:
Online Resource
ISSN:
2375-4699
,
2375-4702
Language:
English
Publisher:
Association for Computing Machinery (ACM)
Publication Date:
2022
detail.hit.zdb_id:
2820615-0
