Predicting Readers’ Sarcasm Understandability by Modeling Gaze Behavior
Authors: Abhijit Mishra, Diptesh Kanojia, Pushpak Bhattacharyya
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | By recording and analyzing the eye-gaze data, we show that eyemovement patterns vary when sarcasm is understood visa-vis when it is not. Motivated by our observations, we propose a system for sarcasm understandability prediction using supervised machine learning. Our system relies on readers eyemovement parameters and a few textual features, thence, is able to predict sarcasm understandability with an F-score of 93%, which demonstrates its efficacy. |
| Researcher Affiliation | Academia | Abhijit Mishra, Diptesh Kanojia, Pushpak Bhattacharyya Center for Indian Language Technology Department of Computer Science and Engineering Indian Institute of Technology Bombay, India, {abhijitmishra, diptesh, pb}@cse.iitb.ac.in |
| Pseudocode | No | The paper describes the proposed system and its components in narrative text but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or a link indicating that the source code for their methodology is publicly available. |
| Open Datasets | Yes | Our database can be freely downloaded3 for academic purposes. [Footnote 3]: http://www.cfilt.iitb.ac.in/cognitive-nlp/. Also, '76 sarcastic short movie reviews were manually extracted from the Amazon Movie Corpus (Pang and Lee )'. |
| Dataset Splits | Yes | After transforming our dataset to a multi-instance dataset, we performed a 5-fold cross validation. Each fold has a train-test split of 80%-20% and each split contains examples from all seven participants. |
| Hardware Specification | No | The paper mentions 'an SR-Research Eyelink-1000 eye-tracker (monocular remote mode, sampling rate 500Hz)' for data collection, but does not provide specific hardware details (like GPU/CPU models or memory) used for running the supervised learning experiments. |
| Software Dependencies | No | The paper states 'We apply Multi-instance Logistic Regression (MILR) ... implemented using the Weka API (Hall et al. 2009)' but does not specify a version number for the Weka API or any other software dependencies. |
| Experiment Setup | No | The paper describes the use of Multi-instance Logistic Regression and a 5-fold cross-validation setup, but does not provide specific hyperparameters or detailed training configurations (e.g., learning rates, batch sizes, number of epochs, or optimizer settings) for the model. |