150 citations · 254 across the 15 of their papers we have counts for
8 papers · 1 filter
Knowledge Distillation from Internal Representations
Gustavo Aguilar, Yuan Ling, Yu Zhang +3
Knowledge distillation is typically conducted by training a small model (the student) to mimic a large and cumbersome model (the teacher). The idea is to compress the knowledge fro…
Dependency-Aware Named Entity Recognition with Relative and Global Attentions
Gustavo Aguilar, Thamar Solorio
Named entity recognition is one of the core tasks in NLP. Although many improvements have been made on this task during the last years, the state-of-the-art systems do not explicit…
From English to Code-Switching: Transfer Learning with Strong Morphological Clues
Gustavo Aguilar, Thamar Solorio
Linguistic Code-switching (CS) is still an understudied phenomenon in natural language processing. The NLP community has mostly focused on monolingual and multi-lingual scenarios,…
Multi-view Story Characterization from Movie Plot Synopses and Reviews
Sudipta Kar, Gustavo Aguilar, Mirella Lapata +1
This paper considers the problem of characterizing stories by inferring properties such as theme and style using written synopses and reviews of movies. We experiment with a multi-…
Multimodal and Multi-view Models for Emotion Recognition
Gustavo Aguilar, Viktor Rozgić, Weiran Wang +1
Studies on emotion recognition (ER) show that combining lexical and acoustic information results in more robust and accurate models. The majority of the studies focus on settings w…
Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task
Gustavo Aguilar, Fahad AlGhamdi, Victor Soto +3
In the third shared task of the Computational Approaches to Linguistic Code-Switching (CALCS) workshop, we focus on Named Entity Recognition (NER) on code-switched social-media dat…
A Multi-task Approach for Named Entity Recognition in Social Media Data
Gustavo Aguilar, Suraj Maharjan, Adrian Pastor López-Monroy +1
Named Entity Recognition for social media data is challenging because of its inherent noisiness. In addition to improper grammatical structures, it contains spelling inconsistencie…
Modeling Noisiness to Recognize Named Entities using Multitask Neural Networks on Social Media
Gustavo Aguilar, A. Pastor López-Monroy, Fabio A. González +1
Recognizing named entities in a document is a key task in many NLP applications. Although current state-of-the-art approaches to this task reach a high performance on clean text (e…