activity
20182022
most citedA Study on the Autoregressive and non-Autoregressive Multi-label Learning

3 citations · 3 across the 1 of their papers we have counts for

collaborators

5 papers

cs.CL2022

Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset

Tiezheng Yu, Rita Frieske, Peng Xu +9

Automatic speech recognition (ASR) on low resource languages improves the access of linguistic minorities to technological advantages provided by artificial intelligence (AI). In t…

cs.LG20203 cited

A Study on the Autoregressive and non-Autoregressive Multi-label Learning

Elham J. Barezi, Iacer Calixto, Kyunghyun Cho +1

Extreme classification tasks are multi-label tasks with an extremely large number of labels (tags). These tasks are hard because the label space is usually (i) very large, e.g. tho…

cs.CL2020

CAiRE-COVID: A Question Answering and Query-focused Multi-Document Summarization System for COVID-19 Scholarly Information Management

Dan Su, Yan Xu, Tiezheng Yu +3

We present CAiRE-COVID, a real-time question answering (QA) and multi-document summarization system, which won one of the 10 tasks in the Kaggle COVID-19 Open Research Dataset Chal…

cs.CL2019

On the Effectiveness of Low-Rank Matrix Factorization for LSTM Model Compression

Genta Indra Winata, Andrea Madotto, Jamin Shin +2

Despite their ubiquity in NLP tasks, Long Short-Term Memory (LSTM) networks suffer from computational inefficiencies caused by inherent unparallelizable recurrences, which further…

cs.AI2018

Investigating Audio, Visual, and Text Fusion Methods for End-to-End Automatic Personality Prediction

Onno Kampman, Elham J. Barezi, Dario Bertero +1

We propose a tri-modal architecture to predict Big Five personality trait scores from video clips with different channels for audio, text, and video data. For each channel, stacked…