23 citations · 68 across the 15 of their papers we have counts for
29 papers
POQue: Asking Participant-specific Outcome Questions for a Deeper Understanding of Complex Events
Sai Vallurupalli, Sayontan Ghosh, Katrin Erk +2
Knowledge about outcomes is critical for complex event understanding but is hard to acquire. We show that by pre-identifying a participant in a complex event, crowd workers are abl…
BioNLI: Generating a Biomedical NLI Dataset Using Lexico-semantic Constraints for Adversarial Examples
Mohaddeseh Bastan, Mihai Surdeanu, Niranjan Balasubramanian
Natural language inference (NLI) is critical for complex decision-making in biomedical domain. One key question, for example, is whether a given biomedical mechanism is supported b…
MeLT: Message-Level Transformer with Masked Document Representations as Pre-Training for Stance Detection
Matthew Matero, Nikita Soni, Niranjan Balasubramanian +1
Much of natural language processing is focused on leveraging large capacity language models, typically trained over single messages with a task of predicting one or more tokens. Ho…
Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension
Naoya Inoue, Harsh Trivedi, Steven Sinha +2
How can we generate concise explanations for multi-hop Reading Comprehension (RC)? The current strategies of identifying supporting sentences can be seen as an extractive question-…
Toward Diverse Precondition Generation
Heeyoung Kwon, Nathanael Chambers, Niranjan Balasubramanian
Language understanding must identify the logical connections between events in a discourse, but core events are often unstated due to their commonsense nature. This paper fills in…
On the Distribution, Sparsity, and Inference-time Quantization of Attention Values in Transformers
Tianchu Ji, Shraddhan Jain, Michael Ferdman +3
How much information do NLP tasks really need from a transformer's attention mechanism at application-time (inference)? From recent work, we know that there is sparsity in transfor…