Papers by Karthik Subbian
Regularized Graph Convolutional Networks for Short Text Classification (2020.coling-industry)
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| Challenge: | Short text classification is a problem in natural language processing, social network analysis, and e-commerce. |
| Approach: | They propose a short text classification technique that incorporates label dependencies into the output space to overcome the limitations of short text. |
| Outcome: | The proposed model outperforms baseline methods on proprietary and external datasets and is more robust to noise in textual features. |
Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment (2022.acl-long)
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Zijie Huang, Zheng Li, Haoming Jiang, Tianyu Cao, Hanqing Lu, Bing Yin, Karthik Subbian, Yizhou Sun, Wei Wang
| Challenge: | Existing methods to predict missing facts in knowledge graphs are limited in language alignment . SS-AGA uses seed alignment as an edge type to fuses all KGs as a whole graph . |
| Approach: | They propose a self-supervised adaptive graph alignment method that fuses all KGs as a whole graph by regarding alignment as 'a new edge type' they propose SS-AGA method that uses relation-aware attention weights to capture potential alignment pairs in a new paradigm. |
| Outcome: | The proposed method can predict missing facts in a knowledge graph (KG) but language alignment is scarce and new alignment identification is noisy. |
Learning Robust Models for e-Commerce Product Search (2020.acl-main)
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| Challenge: | Existing models that understand search intent are difficult to learn due to lack of labeled datasets. |
| Approach: | They develop a deep, end-to-end model that learns to effectively classify mismatches . they introduce a latent variable into the cross-entropy loss that alternates between real and generated samples . |
| Outcome: | The proposed model achieves a relative gain of over 26% in F-score and 17% in Area Under PR curve on live search traffic in multiple countries. |