no code implementations • Findings (ACL) 2021 • Ana Valeria Gonzalez, Anna Rogers, Anders Søgaard
A myriad of explainability methods have been proposed in recent years, but there is little consensus on how to evaluate them.
no code implementations • 29 Jan 2021 • Mostafa Abdou, Ana Valeria Gonzalez, Mariya Toneva, Daniel Hershcovich, Anders Søgaard
We evaluate across two fMRI datasets whether language models align better with brain recordings, if their attention is biased by annotations from syntactic or semantic formalisms.
no code implementations • 30 Dec 2020 • Ana Valeria Gonzalez, Gagan Bansal, Angela Fan, Robin Jia, Yashar Mehdad, Srinivasan Iyer
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 user trust.
2 code implementations • EMNLP 2020 • Ana Valeria Gonzalez, Maria Barrett, Rasmus Hvingelby, Kellie Webster, Anders Søgaard
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 highlight model preferences that are "hallucinatory", e. g., disambiguating gender-ambiguous occurrences of 'doctor' as male doctors.
no code implementations • 30 Sep 2019 • 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.
1 code implementation • 16 Sep 2019 • Joachim Bingel, Victor Petrén Bach Hansen, Ana Valeria Gonzalez, Paweł Budzianowski, Isabelle Augenstein, Anders Søgaard
Task oriented dialogue systems rely heavily on specialized dialogue state tracking (DST) modules for dynamically predicting user intent throughout the conversation.
1 code implementation • IJCNLP 2019 • Rahul Aralikatte, Heather Lent, Ana Valeria Gonzalez, Daniel Hershcovich, Chen Qiu, Anders Sandholm, Michael Ringaard, Anders Søgaard
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.
no code implementations • RANLP 2019 • Meriem Beloucif, Ana Valeria Gonzalez, Marcel Bollmann, Anders S{\o}gaard
Neural machine translation models have little inductive bias, which can be a disadvantage in low-resource scenarios.