Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social Text Classification
Authors: Karish Grover, S M Phaneendra Angara, Md Shad Akhtar, Tanmoy Chakraborty
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on four different social-text classification tasks, namely detecting fake news, hate speech, rumour, and sarcasm, show that Hyphen generalises well, and achieves state-of-the-art results on ten benchmark datasets. |
| Researcher Affiliation | Collaboration | Karish Grover IIIT Delhi India karish19471@iiitd.ac.in S.M. Phaneendra Angara Linked In India sangara@linkedin.com Md. Shad Akhtar IIIT Delhi India shad.akhtar@iiitd.ac.in Tanmoy Chakraborty IIT Delhi India tanchak@ee.iitd.ac.in |
| Pseudocode | No | The paper describes the architecture and processes in text and diagrams but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at: https://github.com/LCS2-IIIIITD/Hyphen. |
| Open Datasets | Yes | We evaluate the performance of Hyphen on four different social-text classification tasks across ten datasets (c.f. Table 1) (i) fake news detection (Politifact [43], Gossipcop [43], Anti Vax [44]), (ii) hate speech detection (HASOC [45]), (iii) rumour detection (Pheme [46], Twitter15 [47], Twitter16 [47], Rumour Eval [48]), and (iv) sarcasm detection (Fig Lang-Twitter[49], Fig Lang-Reddit [49]). |
| Dataset Splits | No | The paper mentions 'early stopping patience of 10 epochs', which implies the use of a validation set, but it does not provide specific details on the training, validation, or test data splits (e.g., percentages or sample counts) in the main text. |
| Hardware Specification | Yes | We run all experiments for 100 epochs with early stopping patience of 10 epochs, on a NVIDIA RTX A6000 GPU. |
| Software Dependencies | No | The paper mentions using 'Riemannian Adam from Geoopt [61]' for optimization, but it does not specify version numbers for Geoopt or any other software dependencies like Python, PyTorch, etc. |
| Experiment Setup | Yes | To find the optimal k (latent dimension, see Equation 5) for hyperbolic co-attention, we run grid search over k = 50, 80, 128, 256, and finally use k = 128. For HGCN, we use two layers with curvatures K1 = K2 = -1. We run all experiments for 100 epochs with early stopping patience of 10 epochs. |