Search Results for author: Edgar Meij

Found 9 papers, 4 papers with code

Improving Dialogue State Tracking with Turn-based Loss Function and Sequential Data Augmentation

1 code implementation Findings (EMNLP) 2021 Jarana Manotumruksa, Jeff Dalton, Edgar Meij, Emine Yilmaz

While state-of-the-art Dialogue State Tracking (DST) models show promising results, all of them rely on a traditional cross-entropy loss function during the training process, which may not be optimal for improving the joint goal accuracy.

Data Augmentation Dialogue State Tracking

News Article Retrieval in Context for Event-centric Narrative Creation

1 code implementation In2Writing (ACL) 2022 Nikos Voskarides, Edgar Meij, Sabrina Sauer, Maarten de Rijke

Given an incomplete narrative that specifies a main event and a context, we aim to retrieve news articles that discuss relevant events that would enable the continuation of the narrative.

Uncertainty over Uncertainty: Investigating the Assumptions, Annotations, and Text Measurements of Economic Policy Uncertainty

no code implementations EMNLP (NLP+CSS) 2020 Katherine A. Keith, Christoph Teichmann, Brendan O'Connor, Edgar Meij

We find for this application (1) some annotator disagreements of economic policy uncertainty can be attributed to ambiguity in language, and (2) switching measurements from keyword-matching to supervised machine learning classifiers results in low correlation, a concerning implication for the validity of the index.

Proceedings of the KG-BIAS Workshop 2020 at AKBC 2020

no code implementations18 Jun 2020 Edgar Meij, Tara Safavi, Chenyan Xiong, Gianluca Demartini, Miriam Redi, Fatma Özcan

The KG-BIAS 2020 workshop touches on biases and how they surface in knowledge graphs (KGs), biases in the source data that is used to create KGs, methods for measuring or remediating bias in KGs, but also identifying other biases such as how and which languages are represented in automatically constructed KGs or how personal KGs might incur inherent biases.

Knowledge Graphs

Evaluating the Calibration of Knowledge Graph Embeddings for Trustworthy Link Prediction

no code implementations EMNLP 2020 Tara Safavi, Danai Koutra, Edgar Meij

We first conduct an evaluation under the standard closed-world assumption (CWA), in which predicted triples not already in the knowledge graph are considered false, and show that existing calibration techniques are effective for KGE under this common but narrow assumption.

Knowledge Graph Embedding Knowledge Graph Embeddings +1

Identifying Notable News Stories

no code implementations16 Mar 2020 Antonia Saravanou, Giorgio Stefanoni, Edgar Meij

The volume of news content has increased significantly in recent years and systems to process and deliver this information in an automated fashion at scale are becoming increasingly prevalent.

Learning-To-Rank

Novel Entity Discovery from Web Tables

1 code implementation1 Feb 2020 Shuo Zhang, Edgar Meij, Krisztian Balog, Ridho Reinanda

We refer to this process as novel entity discovery and, to the best of our knowledge, it is the first endeavor on mining the unlinked cells in web tables.

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