23 citations · 104 across the 21 of their papers we have counts for
7 papers · 1 filter
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-…
MuSiQue: Multihop Questions via Single-hop Question Composition
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot +1
Multihop reasoning remains an elusive goal as existing multihop benchmarks are known to be largely solvable via shortcuts. Can we create a question answering (QA) dataset that, by…
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…
IrEne: Interpretable Energy Prediction for Transformers
Qingqing Cao, Yash Kumar Lal, Harsh Trivedi +2
Existing software-based energy measurements of NLP models are not accurate because they do not consider the complex interactions between energy consumption and model execution. We…