Papers by Trapit Bansal
Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks (2020.emnlp-main)
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| Challenge: | Existing methods for supervised meta-learning require many training tasks to generalize . cloze-style objectives can be used to generate a large, rich, meta-training task distribution from unlabeled text. |
| Approach: | They propose a self-supervised approach to generate a large, rich, meta-learning task distribution from unlabeled text. |
| Outcome: | The proposed approach generates a large, rich, meta-learning task distribution from unlabeled text. |
Marginal Likelihood Training of BiLSTM-CRF for Biomedical Named Entity Recognition from Disjoint Label Sets (D18-1)
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| Challenge: | Existing large labeled text datasets contain labels for multiple subsets of biomedical entity types, but it is rare to find large labeling datasets containing all desired entity types together. |
| Approach: | They propose a method for training a single CRF extractor from multiple datasets with disjoint or partially overlapping sets of entity types. |
| Outcome: | The proposed method improves NER F1 over training in isolation on biocreative V CDR, biocreativ VI ChemProt and MedMentions datasets. |
Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP (2021.emnlp-main)
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| Challenge: | Meta-learning considers learning as an efficient learning process that can leverage its past experience to accurately solve new tasks. |
| Approach: | They propose to provide task distributions for meta-learning by considering self-supervised tasks automatically proposed from unlabeled text to enable large-scale meta- learning in NLP. |
| Outcome: | The proposed distributions show that human learning models perform better on the few-shot benchmark than previous methods. |
Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks (2020.coling-main)
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| Challenge: | Pre-trained transformer models have shown great success in improving performance on downstream tasks, but fine-tuning on a new task still requires large amounts of labeled data. |
| Approach: | They propose a method which allows optimization-based meta-learning across tasks . they use transformers to train transformer models and find better generalizations . |
| Outcome: | The proposed method outperforms self-supervised training and pre-trained models on 17 NLP tasks. |
A2N: Attending to Neighbors for Knowledge Graph Inference (P19-1)
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| Challenge: | Existing knowledge graph completion methods learn a fixed embedding for every entity, which is suboptimal as it requires memorizing and generalizing to all possible entity relationships. |
| Approach: | They propose a method which learns query-dependent representations of entities by combining relevant neighborhood of an entity. |
| Outcome: | The proposed model performs competitively or better than existing state-of-the-art models for knowledge graph completion. |