Papers by Tapas Nayak
Using Sentence-level Classification Helps Entity Extraction from Material Science Literature (2022.lrec-1)
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| Challenge: | Material Science research articles are a rich source of information about entities related to material science. |
| Approach: | They propose to use a sentence-level classifier to identify sentences containing at least one entity mention . they then apply the information extraction models only on the filtered sentences to extract various entities of interest. |
| Outcome: | The proposed model improves the F1 score by more than 4% . the proposed model removes redundant sentences from the articles that contain informative entities . |
A Weak Supervision Approach for Predicting Difficulty of Technical Interview Questions (2022.coling-1)
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| Challenge: | Existing models require large volumes of candidate response data to train . Existing approaches require large amounts of candidate data to generate questions and generate models. |
| Approach: | They create a dataset of interview questions with difficulty scores for deep learning and use it to evaluate SOTA models trained using weak supervision. |
| Outcome: | The proposed model improves the difficulty and promise of weak supervision for interview questions and identifies the potential for weak supervision. |
tagE: Enabling an Embodied Agent to Understand Human Instructions (2023.findings-emnlp)
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| Challenge: | Existing systems for natural language understanding (NLU) are limited due to the inherent ambiguity and incompleteness inherent in natural language. |
| Approach: | They propose a system to extract tasks from natural language instructions and map them to robots' established collection of skills. |
| Outcome: | The proposed system outperforms baseline models in the training and evaluation of a dataset featuring complex instructions. |
PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction (2021.emnlp-main)
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| Challenge: | Existing methods for tagging opinion triplets fail to capture the strong interdependence between the three opinion factors, whereas grid tabbing fails to capture span-level semantics while predicting sentiment between an aspect-opinion pair. |
| Approach: | They propose a tagging-free approach to extracting opinion triplets using a pointer network decoding framework that captures the interdependence between the three elements of an opinion triple. |
| Outcome: | The proposed architecture captures the interdependence between the aspect and opinion triplets while predicting their connecting sentiment. |