352 citations · 471 across the 5 of their papers we have counts for
6 papers · 1 filter
The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
Hugo Laurençon, Lucile Saulnier, Thomas Wang +51
As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings. The BigScience workshop,…
Designing dialogue systems: A mean, grumpy, sarcastic chatbot in the browser
Suzana Ilić, Reiichiro Nakano, Ivo Hajnal
In this work we explore a deep learning-based dialogue system that generates sarcastic and humorous responses from a conversation design perspective. We trained a seq2seq model on…
Deep contextualized word representations for detecting sarcasm and irony
Suzana Ilić, Edison Marrese-Taylor, Jorge A. Balazs +1
Predicting context-dependent and non-literal utterances like sarcastic and ironic expressions still remains a challenging task in NLP, as it goes beyond linguistic patterns, encomp…
IIIDYT at SemEval-2018 Task 3: Irony detection in English tweets
Edison Marrese-Taylor, Suzana Ilic, Jorge A. Balazs +2
In this paper we introduce our system for the task of Irony detection in English tweets, a part of SemEval 2018. We propose representation learning approach that relies on a multi-…
Putting Question-Answering Systems into Practice: Transfer Learning for Efficient Domain Customization
Bernhard Kratzwald, Stefan Feuerriegel
Traditional information retrieval (such as that offered by web search engines) impedes users with information overload from extensive result pages and the need to manually locate t…
Deep learning for affective computing: text-based emotion recognition in decision support
Bernhard Kratzwald, Suzana Ilic, Mathias Kraus +2
Emotions widely affect human decision-making. This fact is taken into account by affective computing with the goal of tailoring decision support to the emotional states of individu…