184 citations · 185 across the 3 of their papers we have counts for
8 papers
OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data
Shubham Toshniwal, Wei Du, Ivan Moshkov +3
Mathematical reasoning continues to be a critical challenge in large language model (LLM) development with significant interest. However, most of the cutting-edge progress in mathe…
On Generalization in Coreference Resolution
Shubham Toshniwal, Patrick Xia, Sam Wiseman +2
While coreference resolution is defined independently of dataset domain, most models for performing coreference resolution do not transfer well to unseen domains. We consolidate a…
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…
A Cross-Task Analysis of Text Span Representations
Shubham Toshniwal, Haoyue Shi, Bowen Shi +3
Many natural language processing (NLP) tasks involve reasoning with textual spans, including question answering, entity recognition, and coreference resolution. While extensive res…
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…
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Jonathan Shen, Patrick Nguyen, Yonghui Wu +88
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models a…