AttViz: Online exploration of self-attention for transparent neural language modeling

1 code implementation • 12 May 2020 • Blaž Škrlj, Nika Eržen, Shane Sheehan, Saturnino Luz, Marko Robnik-Šikonja, Senja Pollak

Neural language models are becoming the prevailing methodology for the tasks of query answering, text classification, disambiguation, completion and translation.

Language Modelling text-classification +2

TeMoTopic: Temporal Mosaic Visualisation of Topic Distribution, Keywords, and Context

no code implementations • EACL (Hackashop) 2021 • Shane Sheehan, Saturnino Luz, Masood Masoodian

In this paper we present TeMoTopic, a visualization component for temporal exploration of topics in text corpora.

Exploring Neural Language Models via Analysis of Local and Global Self-Attention Spaces

1 code implementation • EACL (Hackashop) 2021 • Blaž Škrlj, Shane Sheehan, Nika Eržen, Marko Robnik-Šikonja, Saturnino Luz, Senja Pollak

Large pretrained language models using the transformer neural network architecture are becoming a dominant methodology for many natural language processing tasks, such as question answering, text classification, word sense disambiguation, text completion and machine translation.

Machine Translation Question Answering +4

EMBEDDIA Tools, Datasets and Challenges: Resources and Hackathon Contributions

no code implementations • EACL (Hackashop) 2021 • Senja Pollak, Marko Robnik-Šikonja, Matthew Purver, Michele Boggia, Ravi Shekhar, Marko Pranjić, Salla Salmela, Ivar Krustok, Tarmo Paju, Carl-Gustav Linden, Leo Leppänen, Elaine Zosa, Matej Ulčar, Linda Freienthal, Silver Traat, Luis Adrián Cabrera-Diego, Matej Martinc, Nada Lavrač, Blaž Škrlj, Martin Žnidaršič, Andraž Pelicon, Boshko Koloski, Vid Podpečan, Janez Kranjc, Shane Sheehan, Emanuela Boros, Jose G. Moreno, Antoine Doucet, Hannu Toivonen

This paper presents tools and data sources collected and released by the EMBEDDIA project, supported by the European Union’s Horizon 2020 research and innovation program.