71 citations · 125 across the 21 of their papers we have counts for
6 papers · 1 filter
Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks
Shubham Toshniwal, Sam Wiseman, Allyson Ettinger +2
Long document coreference resolution remains a challenging task due to the large memory and runtime requirements of current models. Recent work doing incremental coreference resolu…
Assessing Phrasal Representation and Composition in Transformers
Lang Yu, Allyson Ettinger
Deep transformer models have pushed performance on NLP tasks to new limits, suggesting sophisticated treatment of complex linguistic inputs, such as phrases. However, we have limit…
Exploring BERT's Sensitivity to Lexical Cues using Tests from Semantic Priming
Kanishka Misra, Allyson Ettinger, Julia Taylor Rayz
Models trained to estimate word probabilities in context have become ubiquitous in natural language processing. How do these models use lexical cues in context to inform their word…
Adding Recurrence to Pretrained Transformers for Improved Efficiency and Context Size
Davis Yoshida, Allyson Ettinger, Kevin Gimpel
Fine-tuning a pretrained transformer for a downstream task has become a standard method in NLP in the last few years. While the results from these models are impressive, applying t…
PeTra: A Sparsely Supervised Memory Model for People Tracking
Shubham Toshniwal, Allyson Ettinger, Kevin Gimpel +1
We propose PeTra, a memory-augmented neural network designed to track entities in its memory slots. PeTra is trained using sparse annotation from the GAP pronoun resolution dataset…
Spying on your neighbors: Fine-grained probing of contextual embeddings for information about surrounding words
Josef Klafka, Allyson Ettinger
Although models using contextual word embeddings have achieved state-of-the-art results on a host of NLP tasks, little is known about exactly what information these embeddings enco…