Papers by Alakananda Vempala
Right for the Right Reason: Evidence Extraction for Trustworthy Tabular Reasoning (2022.acl-long)
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| Challenge: | Recent studies show that tabular reasoning models use spurious correlations and focus on false evidence or ignore it altogether. |
| Approach: | They propose a task where models need to extract evidence and then inference labels . they crowdsource evidence row labels and develop unsupervised evidence extraction strategies . |
| Outcome: | The proposed approach outperforms baseline models on the inference task using only the automatically extracted evidence as the premise. |
Annotating If the Authors of a Tweet are Located at the Locations They Tweet About (L18-1)
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| Challenge: | a tweet's locations do not always indicate spatial information involving the author of the tweet . a corpus of 1,062 tweets contains 1,200 location named entities . |
| Approach: | They propose a corpus annotating whether tweet authors are located in locations . they use temporal tags centered around tweet timestamps to temporally anchor this information . |
| Outcome: | The proposed method annotates whether authors are located in tweet locations . it shows that no spatial relationship can be inferred in 21% of instances . |
Determining Event Durations: Models and Error Analysis (N18-2)
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| Challenge: | a crucial piece of information regarding events is their duration, a rarely mentioned attribute . core tasks such as temporal understanding and reasoning would benefit from knowing the expected duration of events. |
| Approach: | They introduce aspectual features that capture deeper linguistic information . they also experiment with neural networks to predict event durations . |
| Outcome: | The proposed models capture deeper linguistic information than previous work and provide useful clues. |
Categorizing and Inferring the Relationship between the Text and Image of Twitter Posts (P19-1)
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| Challenge: | Social media posts often contain images to provide content, provide context, or express feelings. |
| Approach: | They build and release a dataset of image tweets annotated with four different classes which express whether the text or the image provides additional information to the other modality. |
| Outcome: | The proposed method can be used in several downstream applications including pre-training image tagging models and collecting distantly supervised data for image captioning. |
Don’t Retrain, Just Rewrite: Countering Adversarial Perturbations by Rewriting Text (2023.acl-long)
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| Challenge: | ATINTER model can be used to rewrite adversarial inputs to make them non-adversarial . if undefended, model should maintain good task performance and effectively mitigate adversarials . |
| Approach: | They propose a model that intercepts adversarial inputs and learns to rewrite them . they show that it provides better adversarial robustness than existing defense approaches . |
| Outcome: | The proposed model improves adversarial robustness without compromising task accuracy on a sentiment classification dataset. |
ULTRA: Unleash LLMs’ Potential for Event Argument Extraction through Hierarchical Modeling and Pair-wise Self-Refinement (2024.findings-acl)
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| Challenge: | Structural extraction of events within discourse is critical for event-centric understanding . document-level EAE focuses on arguments that are scattered across an entire document . ULTRA is a hierarchical framework that extracts event arguments more cost-effectively . |
| Approach: | They propose a hierarchical framework that extracts event arguments more cost-effectively . ULTRA sequentially reads text chunks of a document to generate a candidate argument set . they propose to use a supervised model to find the exact boundary of an argument . |
| Outcome: | The proposed framework outperforms strong models and ChatGPT by 9.8% when evaluated by Exact Match (EM). |
Annotating Temporally-Anchored Spatial Knowledge by Leveraging Syntactic Dependencies (L18-1)
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| Challenge: | Existing approaches to extract spatial knowledge focus on extracting locations of events, someone or something. |
| Approach: | They propose a method to annotate temporally-anchored spatial knowledge on top of OntoNotes by crowdsourcing annotations. |
| Outcome: | The proposed method can be automated and validated using syntactic dependencies and crowdsourced annotations. |