21 citations · 30 across the 9 of their papers we have counts for
9 papers
Summarization from Leaderboards to Practice: Choosing A Representation Backbone and Ensuring Robustness
David Demeter, Oshin Agarwal, Simon Ben Igeri +4
Academic literature does not give much guidance on how to build the best possible customer-facing summarization system from existing research components. Here we present analyses t…
Temporal Effects on Pre-trained Models for Language Processing Tasks
Oshin Agarwal, Ani Nenkova
Keeping the performance of language technologies optimal as time passes is of great practical interest. We study temporal effects on model performance on downstream language tasks,…
From Toxicity in Online Comments to Incivility in American News: Proceed with Caution
Anushree Hede, Oshin Agarwal, Linda Lu +2
The ability to quantify incivility online, in news and in congressional debates, is of great interest to political scientists. Computational tools for detecting online incivility f…
Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training
Oshin Agarwal, Heming Ge, Siamak Shakeri +1
Prior work on Data-To-Text Generation, the task of converting knowledge graph (KG) triples into natural text, focused on domain-specific benchmark datasets. In this paper, however,…
Entity Linking via Dual and Cross-Attention Encoders
Oshin Agarwal, Daniel M. Bikel
Entity Linking has two main open areas of research: 1) generate candidate entities without using alias tables and 2) generate more contextual representations for both mentions and…
Interpretability Analysis for Named Entity Recognition to Understand System Predictions and How They Can Improve
Oshin Agarwal, Yinfei Yang, Byron C. Wallace +1
Named Entity Recognition systems achieve remarkable performance on domains such as English news. It is natural to ask: What are these models actually learning to achieve this? Are…
Entity-Switched Datasets: An Approach to Auditing the In-Domain Robustness of Named Entity Recognition Models
Oshin Agarwal, Yinfei Yang, Byron C. Wallace +1
Named entity recognition systems perform well on standard datasets comprising English news. But given the paucity of data, it is difficult to draw conclusions about the robustness…
Predicting Annotation Difficulty to Improve Task Routing and Model Performance for Biomedical Information Extraction
Yinfei Yang, Oshin Agarwal, Chris Tar +2
Modern NLP systems require high-quality annotated data. In specialized domains, expert annotations may be prohibitively expensive. An alternative is to rely on crowdsourcing to red…
Named Person Coreference in English News
Oshin Agarwal, Sanjay Subramanian, Ani Nenkova +1
People are often entities of interest in tasks such as search and information extraction. In these tasks, the goal is to find as much information as possible about people specified…