activity
20162022
most citedWord Interdependence Exposes How LSTMs Compose Representations

1 citations · 2 across the 2 of their papers we have counts for

collaborators

6 papers

cs.DL20221 cited

One Venue, Two Conferences: The Separation of Chinese and American Citation Networks

Bingchen Zhao, Yuling Gu, Jessica Zosa Forde +1

At NeurIPS, American and Chinese institutions cite papers from each other's regions substantially less than they cite endogamously. We build a citation graph to quantify this divid…

cs.CL2021

A Non-Linear Structural Probe

Jennifer C. White, Tiago Pimentel, Naomi Saphra +1

Probes are models devised to investigate the encoding of knowledge -- e.g. syntactic structure -- in contextual representations. Probes are often designed for simplicity, which has…

cs.CL2020

LSTMs Compose (and Learn) Bottom-Up

Naomi Saphra, Adam Lopez

Recent work in NLP shows that LSTM language models capture hierarchical structure in language data. In contrast to existing work, we consider the \textit{learning} process that lea…

cs.CL20201 cited

Word Interdependence Exposes How LSTMs Compose Representations

Naomi Saphra, Adam Lopez

Recent work in NLP shows that LSTM language models capture compositional structure in language data. For a closer look at how these representations are composed hierarchically, we…

cs.CL2019

Sparsity Emerges Naturally in Neural Language Models

Naomi Saphra, Adam Lopez

Concerns about interpretability, computational resources, and principled inductive priors have motivated efforts to engineer sparse neural models for NLP tasks. If sparsity is impo…

cs.CL2016

Evaluating Informal-Domain Word Representations With UrbanDictionary

Naomi Saphra, Adam Lopez

Existing corpora for intrinsic evaluation are not targeted towards tasks in informal domains such as Twitter or news comment forums. We want to test whether a representation of inf…