125 citations · 366 across the 33 of their papers we have counts for
6 papers · 1 filter
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 IEST 2018: Implicit Emotion Classification With Deep Contextualized Word Representations
Jorge A. Balazs, Edison Marrese-Taylor, Yutaka Matsuo
In this paper we describe our system designed for the WASSA 2018 Implicit Emotion Shared Task (IEST), which obtained 2 place out of 26 teams with a test macro F1 scor…
Learning to Automatically Generate Fill-In-The-Blank Quizzes
Edison Marrese-Taylor, Ai Nakajima, Yutaka Matsuo +1
In this paper we formalize the problem automatic fill-in-the-blank question generation using two standard NLP machine learning schemes, proposing concrete deep learning models for…
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-…
Expressive Speech Synthesis via Modeling Expressions with Variational Autoencoder
Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo
Recent advances in neural autoregressive models have improve the performance of speech synthesis (SS). However, as they lack the ability to model global characteristics of speech (…
Improving Bi-directional Generation between Different Modalities with Variational Autoencoders
Masahiro Suzuki, Kotaro Nakayama, Yutaka Matsuo
We investigate deep generative models that can exchange multiple modalities bi-directionally, e.g., generating images from corresponding texts and vice versa. A major approach to a…