12 citations · 29 across the 24 of their papers we have counts for
12 papers · 1 filter
Toucan: Token-Aware Character Level Language Modeling
William Fleshman, Benjamin Van Durme
Character-level language models obviate the need for separately trained tokenizers, but efficiency suffers from longer sequence lengths. Learning to combine character representatio…
FAMuS: Frames Across Multiple Sources
Siddharth Vashishtha, Alexander Martin, William Gantt +2
Understanding event descriptions is a central aspect of language processing, but current approaches focus overwhelmingly on single sentences or documents. Aggregating information a…
Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles
Weiting Tan, Haoran Xu, Lingfeng Shen +5
Large language models trained primarily in a monolingual setting have demonstrated their ability to generalize to machine translation using zero- and few-shot examples with in-cont…
InstructExcel: A Benchmark for Natural Language Instruction in Excel
Justin Payan, Swaroop Mishra, Mukul Singh +7
With the evolution of Large Language Models (LLMs) we can solve increasingly more complex NLP tasks across various domains, including spreadsheets. This work investigates whether L…
A Unified View of Evaluation Metrics for Structured Prediction
Yunmo Chen, William Gantt, Tongfei Chen +2
We present a conceptual framework that unifies a variety of evaluation metrics for different structured prediction tasks (e.g. event and relation extraction, syntactic and semantic…
Nugget: Neural Agglomerative Embeddings of Text
Guanghui Qin, Benjamin Van Durme
Embedding text sequences is a widespread requirement in modern language understanding. Existing approaches focus largely on constant-size representations. This is problematic, as t…