Papers by Nithish Kannen
Efficient Pointwise-Pairwise Learning-to-Rank for News Recommendation (2024.findings-emnlp)
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| Challenge: | Recent work leverages the power of pretrained language models to rank news items . pointwise approaches fail to capture comparative information between items that is more effective for ranking tasks. |
| Approach: | They propose a framework for PLM-based news recommendation that integrates pointwise relevance prediction and pairwise comparisons in a scalable manner. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on the MIND and Adressa news recommendation datasets. |
CONTRASTE: Supervised Contrastive Pre-training With Aspect-based Prompts For Aspect Sentiment Triplet Extraction (2023.findings-emnlp)
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| Challenge: | Existing studies on Aspect Sentiment Triplet Extraction focus on developing more efficient techniques for the task, but our proposed approach can improve the downstream performance of multiple ABSA tasks simultaneously. |
| Approach: | They propose a novel approach that uses contrastive learning to enhance the ASTE performance by masked sentiments. |
| Outcome: | The proposed approach improves the performance of multiple ABSA tasks simultaneously. |
Best of Both Worlds: Towards Improving Temporal Knowledge Base Question Answering via Targeted Fact Extraction (2023.emnlp-main)
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Nithish Kannen, Udit Sharma, Sumit Neelam, Dinesh Khandelwal, Shajith Ikbal, Hima Karanam, L Subramaniam
| Challenge: | Temporal question answering (QA) is a complex task that requires reasoning over facts asserting time intervals of events. |
| Approach: | They propose a temporal fact extraction technique that helps QA when it fails to retrieve temporal facts from the KB. |
| Outcome: | The proposed technique can extract temporal facts that failed to get retrieved from the KB without additional training cost. |