53 citations · 99 across the 5 of their papers we have counts for
10 papers
EvEntS ReaLM: Event Reasoning of Entity States via Language Models
Evangelia Spiliopoulou, Artidoro Pagnoni, Yonatan Bisk +1
This paper investigates models of event implications. Specifically, how well models predict entity state-changes, by targeting their understanding of physical attributes. Nominally…
Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics
Artidoro Pagnoni, Vidhisha Balachandran, Yulia Tsvetkov
Modern summarization models generate highly fluent but often factually unreliable outputs. This motivated a surge of metrics attempting to measure the factuality of automatically g…
StructSum: Summarization via Structured Representations
Vidhisha Balachandran, Artidoro Pagnoni, Jay Yoon Lee +3
Abstractive text summarization aims at compressing the information of a long source document into a rephrased, condensed summary. Despite advances in modeling techniques, abstracti…
Definition Frames: Using Definitions for Hybrid Concept Representations
Evangelia Spiliopoulou, Artidoro Pagnoni, Eduard Hovy
Advances in word representations have shown tremendous improvements in downstream NLP tasks, but lack semantic interpretability. In this paper, we introduce Definition Frames (DF),…
Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach
Gyeong-In Yu, Saeed Amizadeh, Sehoon Kim +4
Classical Machine Learning (ML) pipelines often comprise of multiple ML models where models, within a pipeline, are trained in isolation. Conversely, when training neural network m…
Machine Learning at Microsoft with ML .NET
Zeeshan Ahmed, Saeed Amizadeh, Mikhail Bilenko +31
Machine Learning is transitioning from an art and science into a technology available to every developer. In the near future, every application on every platform will incorporate t…