Papers by Rishita Anubhai
Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events (2020.emnlp-main)
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Miguel Ballesteros, Rishita Anubhai, Shuai Wang, Nima Pourdamghani, Yogarshi Vyas, Jie Ma, Parminder Bhatia, Kathleen McKeown, Yaser Al-Onaizan
| Challenge: | Existing models for temporal ordering of events rely on pretrained representations, transfer and multitask learning, and self-training techniques. |
| Approach: | They propose a neural architecture and a set of training methods for ordering events by predicting temporal relations by pre-training models. |
| Outcome: | The proposed models can predict temporal relations between two pairs of events within a span of text and identify temporal relationships between them. |
Label Semantics for Few Shot Named Entity Recognition (2022.findings-acl)
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Jie Ma, Miguel Ballesteros, Srikanth Doss, Rishita Anubhai, Sunil Mallya, Yaser Al-Onaizan, Dan Roth
| Challenge: | Named entity recognition (NER) is a fundamental natural language understanding task that requires large amounts of high quality annotated in-domain data. |
| Approach: | They propose a neural architecture that leverages the semantic information in the names of the labels to give the model additional signal and enriched priors. |
| Outcome: | The proposed model is especially effective in low resource settings. |
Resource-Enhanced Neural Model for Event Argument Extraction (2020.findings-emnlp)
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| Challenge: | Existing work on event argument extraction (EE) is limited due to data scarcity and lack of a model encoder. |
| Approach: | They propose to capture the long-range dependency between an event trigger and a distant event argument using unlabeled data. |
| Outcome: | Experiments on the English ACE 2005 benchmark show that the proposed method achieves a new state-of-the-art. |
To BERT or Not to BERT: Comparing Task-specific and Task-agnostic Semi-Supervised Approaches for Sequence Tagging (2020.emnlp-main)
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Kasturi Bhattacharjee, Miguel Ballesteros, Rishita Anubhai, Smaranda Muresan, Jie Ma, Faisal Ladhak, Yaser Al-Onaizan
| Challenge: | Using large amounts of unlabeled data to improve performance has become the foundation for many natural language processing tasks. |
| Approach: | They propose a task-specific semi-supervised approach that uses unlabeled data in a more task-agnostic manner. |
| Outcome: | The proposed approach achieves similar performance to BERT on a set of sequence tagging tasks with less financial and environmental impact. |
Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction (2021.findings-acl)
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Elsbeth Turcan, Shuai Wang, Rishita Anubhai, Kasturi Bhattacharjee, Yaser Al-Onaizan, Smaranda Muresan
| Challenge: | Detecting what emotions are expressed in text is a well-studied problem in natural language processing. |
| Approach: | They propose methods that combine common-sense knowledge with multi-task learning to perform joint emotion classification and emotion cause tagging. |
| Outcome: | The proposed models improve on both tasks when using common-sense reasoning and a multitask framework. |