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Ingredients for Happiness: Modeling constructs via semi-supervised content driven inductive transfer learningAuthors: Bakhtiyar Syed,Vijaysaradhi Indurthi,kulin Shah,Manish Gupta,Vasudeva Varma Conference: THE AAAI-19 WORKSHOP ON AFFECTIVE CONTENT ANALYSIS (AFFCON-2019 2019) Location Hilton Hawaiian Village, Honolulu, Hawaii, USA Date: 2019-01-27 Report no: IIIT/TR/2019/99 AbstractModeling affect via understanding the social constructs behind them is an important task in devising robust and accurate systems for socially relevant scenarios. In the CL-Aff Shared Task (part of Affective Content Analysis workshop @ AAAI 2019), the organizers released a dataset of ‘happy’ moments, called the HappyDB corpus. The task is to detect two social constructs: the agency (i.e., whether the author is in control of the happy moment) and the social characteristics (i.e., whether anyone else other than the author was also involved in the happy moment). We employ an inductive transfer learning technique where we utilize a pre-trained language model and fine-tune it on the target task for both the binary classification tasks. At first, we use a language model pre-trained on the huge WikiText-103 corpus. This step utilizes an AWD-LSTM with three hidden layers for training the language model. In the second step, we fine-tune the pre-trained language model on both the labeled and unlabeled instances from the HappyDB dataset. Finally, we train a classifier on top of the language model for each of the identification tasks. Our experiments using 10-fold cross validation on the corpus show that we achieve a high accuracy of ∼93% for detection of the social characteristic and ∼87% for agency of the author, showing significant gains over other baselines. We also show that using the unlabeled dataset for fine-tuning the language model in the second step improves our accuracy by 1-2% across detection of both the constructs. Full paper: pdf Centre for Search and Information Extraction Lab |
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