activity
20192022
most citedA Multi-task Approach for Named Entity Recognition in Social Media Data

150 citations · 244 across the 11 of their papers we have counts for

collaborators

16 papers

cs.CL20221 cited

Self-Aware Feedback-Based Self-Learning in Large-Scale Conversational AI

Pragaash Ponnusamy, Clint Solomon Mathialagan, Gustavo Aguilar +2

Self-learning paradigms in large-scale conversational AI agents tend to leverage user feedback in bridging between what they say and what they mean. However, such learning, particu…

cs.CL2022

A Vocabulary-Free Multilingual Neural Tokenizer for End-to-End Task Learning

Md Mofijul Islam, Gustavo Aguilar, Pragaash Ponnusamy +3

Subword tokenization is a commonly used input pre-processing step in most recent NLP models. However, it limits the models' ability to leverage end-to-end task learning. Its freque…

cs.CL202210 cited

CALCS 2021 Shared Task: Machine Translation for Code-Switched Data

Shuguang Chen, Gustavo Aguilar, Anirudh Srinivasan +2

To date, efforts in the code-switching literature have focused for the most part on language identification, POS, NER, and syntactic parsing. In this paper, we address machine tran…

cs.CL20211 cited

Data Augmentation for Cross-Domain Named Entity Recognition

Shuguang Chen, Gustavo Aguilar, Leonardo Neves +1

Current work in named entity recognition (NER) shows that data augmentation techniques can produce more robust models. However, most existing techniques focus on augmenting in-doma…

cs.CL2020

Char2Subword: Extending the Subword Embedding Space Using Robust Character Compositionality

Gustavo Aguilar, Bryan McCann, Tong Niu +3

Byte-pair encoding (BPE) is a ubiquitous algorithm in the subword tokenization process of language models as it provides multiple benefits. However, this process is solely based on…

cs.CL2020

Can images help recognize entities? A study of the role of images for Multimodal NER

Shuguang Chen, Gustavo Aguilar, Leonardo Neves +1

Multimodal named entity recognition (MNER) requires to bridge the gap between language understanding and visual context. While many multimodal neural techniques have been proposed…