Alexandra Olteanu

Associate Industry Member

Principal Researcher and founding members of the FATE Montréal Team, Microsoft Research, Montréal

Research Topics

Information Retrieval

Natural Language Processing

Website

Google Scholar

Publications

Human-Centered Responsible Artificial Intelligence: Current & Future Trends

Mohammad Tahaei

Marios Constantinides

Daniele Quercia

Sean Kennedy

Michael Muller

Simone Stumpf

Q. Vera Liao

Ricardo Baeza-Yates

Lora Aroyo

Jess Holbrook

Ewa Luger

Michael Madaio

Ilana Golbin Blumenfeld

Maria De-Arteaga

Jessica Vitak

Alexandra Olteanu

2023-01-01

CHI Extended Abstracts (published)

doi.org

arxiv.org

Responsible AI Considerations in Text Summarization Research: A Review of Current Practices

Yu Lu Liu

Meng Cao

Su Lin Blodgett

Jackie Cheung

Alexandra Olteanu

Adam Trischler

2023-01-01

EMNLP (Findings) (published)

doi.org

openreview.net

The KITMUS Test: Evaluating Knowledge Integration from Multiple Sources

Akshatha Arodi

Martin Pömsl

Kaheer Suleman

Adam Trischler

Alexandra Olteanu

Jackie Cheung

Many state-of-the-art natural language understanding (NLU) models are based on pretrained neural language models. These models often make in… (see more)ferences using information from multiple sources. An important class of such inferences are those that require both background knowledge, presumably contained in a model’s pretrained parameters, and instance-specific information that is supplied at inference time. However, the integration and reasoning abilities of NLU models in the presence of multiple knowledge sources have been largely understudied. In this work, we propose a test suite of coreference resolution subtasks that require reasoning over multiple facts. These subtasks differ in terms of which knowledge sources contain the relevant facts. We also introduce subtasks where knowledge is present only at inference time using fictional knowledge. We evaluate state-of-the-art coreference resolution models on our dataset. Our results indicate that several models struggle to reason on-the-fly over knowledge observed both at pretrain time and at inference time. However, with task-specific training, a subset of models demonstrates the ability to integrate certain knowledge types from multiple sources. Still, even the best performing models seem to have difficulties with reliably integrating knowledge presented only at inference time.

2022-12-15

ArXiv (preprint)

doi.org

arxiv.org

ADEPT: An Adjective-Dependent Plausibility Task

Ali Emami

Ian Porada

Alexandra Olteanu

Kaheer Suleman

Adam Trischler

Jackie Cheung

2021-01-01