activity
20182021
most citedDoes injecting linguistic structure into language models lead to better alignment with brain recordings?

19 citations · 28 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CL202119 cited

Does injecting linguistic structure into language models lead to better alignment with brain recordings?

Mostafa Abdou, Ana Valeria Gonzalez, Mariya Toneva +2

Neuroscientists evaluate deep neural networks for natural language processing as possible candidate models for how language is processed in the brain. These models are often traine…

cs.CL20207 cited

Human Evaluation of Spoken vs. Visual Explanations for Open-Domain QA

Ana Valeria Gonzalez, Gagan Bansal, Angela Fan +3

While research on explaining predictions of open-domain QA systems (ODQA) to users is gaining momentum, most works have failed to evaluate the extent to which explanations improve…

cs.CL2020

Type B Reflexivization as an Unambiguous Testbed for Multilingual Multi-Task Gender Bias

Ana Valeria Gonzalez, Maria Barrett, Rasmus Hvingelby +2

The one-sided focus on English in previous studies of gender bias in NLP misses out on opportunities in other languages: English challenge datasets such as GAP and WinoGender highl…

cs.CL20192 cited

Retrieval-based Goal-Oriented Dialogue Generation

Ana Valeria Gonzalez, Isabelle Augenstein, Anders Søgaard

Most research on dialogue has focused either on dialogue generation for openended chit chat or on state tracking for goal-directed dialogue. In this work, we explore a hybrid appro…

cs.CL2019

Domain Transfer in Dialogue Systems without Turn-Level Supervision

Joachim Bingel, Victor Petrén Bach Hansen, Ana Valeria Gonzalez +3

Task oriented dialogue systems rely heavily on specialized dialogue state tracking (DST) modules for dynamically predicting user intent throughout the conversation. State-of-the-ar…

cs.CL2019

Rewarding Coreference Resolvers for Being Consistent with World Knowledge

Rahul Aralikatte, Heather Lent, Ana Valeria Gonzalez +5

Unresolved coreference is a bottleneck for relation extraction, and high-quality coreference resolvers may produce an output that makes it a lot easier to extract knowledge triples…