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Neha Srikanth

6 accepted papers

2025

No Questions are Stupid, but some are Poorly Posed: Understanding Poorly-Posed Information-Seeking Questions

ACL 2025long

Questions help unlock information to satisfy users’ information needs. However, when the question is poorly posed, answerers (whether human or computer) may struggle to answer the question in a way that satisfies the asker, despite possibly knowing everything necessary to address the asker’s latent…

2025

SQLSpace: A Representation Space for Text-to-SQL to Discover and Mitigate Robustness Gaps

EMNLP 2025

We introduce SQLSpace, a human-interpretable, generalizable, compact representation for text-to-SQL examples derived with minimal human intervention. We demonstrate the utility of these representations in evaluation with three use cases: (i) closely comparing and contrasting the composition of popul

2025

Understanding Common Ground Misalignment in Goal-Oriented Dialog: A Case-Study with Ubuntu Chat Logs

ACL 2025long

While it is commonly accepted that maintaining common ground plays a role in conversational success, little prior research exists connecting conversational grounding to success in task-oriented conversations. We study failures of grounding in the Ubuntu IRC dataset, where participants use text-only…

Cited by 0SourcePDFScholar
2024

Pregnant Questions: The Importance of Pragmatic Awareness in Maternal Health Question Answering

NAACL 2024long

Questions posed by information-seeking users often contain implicit false or potentially harmful assumptions. In a high-risk domain such as maternal and infant health, a question-answering system must recognize these pragmatic constraints and go beyond simply answering user questions, examining them…

2022

Partial-input baselines show that NLI models can ignore context, but they don’t.

NAACL 2022long

When strong partial-input baselines reveal artifacts in crowdsourced NLI datasets, the performance of full-input models trained on such datasets is often dismissed as reliance on spurious correlations. We investigate whether state-of-the-art NLI models are capable of overriding default inferences ma…