Papers by Yatin Chaudhary
Multi-source Neural Topic Modeling in Multi-view Embedding Spaces (2021.naacl-main)
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| Challenge: | Recent work has used pre-trained word embeddings to address data sparsity in short-text or small document collections. |
| Approach: | They propose a neural topic modeling framework using multi-view embedding spaces to improve topic quality and deal with polysemy. |
| Outcome: | The proposed framework improves topic quality and deal with polysemy. |
BioNLP-OST 2019 RDoC Tasks: Multi-grain Neural Relevance Ranking Using Topics and Attention Based Query-Document-Sentence Interactions (D19-57)
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| Challenge: | Our best systems achieved 1st rank and scored 0.86 mAP and 0.58 macro average accuracy in Task-1 and Task-2 respectively. |
| Approach: | They propose to use attention-based supervised neural topic model and SVM for retrieval and ranking of PubMed abstracts and to use BM25 and other relevance measures for re-ranking. |
| Outcome: | The proposed system scored 0.86 mAP and 0.58 macro average accuracy in the RDoC Tasks of BioNLP-OST 2019 . |
TopicBERT for Energy Efficient Document Classification (2020.findings-emnlp)
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| Challenge: | Prior work has noted that BERT’s computational cost grows quadratically with sequence length thus leading to longer training times, higher GPU memory constraints and carbon emissions. |
| Approach: | They propose to combine topic and language models to optimize the computational cost of fine-tuning for document classification by complementary learning. |
| Outcome: | The proposed model achieves a 1.4x speedup with 40% reduction in CO2 emission while retaining 99.9% performance over 5 datasets. |
Federated Continual Learning for Text Classification via Selective Inter-client Transfer (2022.findings-emnlp)
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| Challenge: | Continual Learning (CL) is a privacy-preserving machine learning technique that enables collaborative training of ML models by sharing model parameters across distributed clients. |
| Approach: | They propose a framework which selectively combines model parameters of foreign clients to maximize knowledge transfer while preserving privacy. |
| Outcome: | The proposed framework improves the performance of a text classification task using five datasets from diverse domains while preserving privacy. |