9 citations · 17 across the 7 of their papers we have counts for
8 papers
Two-Turn Debate Doesn't Help Humans Answer Hard Reading Comprehension Questions
Alicia Parrish, Harsh Trivedi, Nikita Nangia +4
The use of language-model-based question-answering systems to aid humans in completing difficult tasks is limited, in part, by the unreliability of the text these systems generate.…
Single-Turn Debate Does Not Help Humans Answer Hard Reading-Comprehension Questions
Alicia Parrish, Harsh Trivedi, Ethan Perez +4
Current QA systems can generate reasonable-sounding yet false answers without explanation or evidence for the generated answer, which is especially problematic when humans cannot r…
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-…
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…
What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks?
Nikita Nangia, Saku Sugawara, Harsh Trivedi +3
Crowdsourcing is widely used to create data for common natural language understanding tasks. Despite the importance of these datasets for measuring and refining model understanding…
DeFormer: Decomposing Pre-trained Transformers for Faster Question Answering
Qingqing Cao, Harsh Trivedi, Aruna Balasubramanian +1
Transformer-based QA models use input-wide self-attention -- i.e. across both the question and the input passage -- at all layers, causing them to be slow and memory-intensive. It…