no code implementations • 26 Jul 2017 • Noseong Park, Ankesh Anand, Joel Ruben Antony Moniz, Kookjin Lee, Tanmoy Chakraborty, Jaegul Choo, Hongkyu Park, Young-Min Kim
MMGAN finds two manifolds representing the vector representations of real and fake images.
no code implementations • 20 Apr 2019 • Joel Ruben Antony Moniz, Barun Patra, Sarthak Garg
Deep neural networks have become commonplace in the domain of reinforcement learning, but are often expensive in terms of the number of parameters needed.
no code implementations • IJCNLP 2019 • Barun Patra, Joel Ruben Antony Moniz
The task of entity recognition has traditionally been modelled as a sequence labelling task.
no code implementations • 27 Nov 2019 • Joel Ruben Antony Moniz, Eunsu Kang, Barnabás Póczos
In this work, we aim to propose a set of techniques to improve the controllability and aesthetic appeal when DeepDream, which uses a pre-trained neural network to modify images by hallucinating objects into them, is applied to videos.
no code implementations • ACL 2019 • Sarthak Garg, Joel Ruben Antony Moniz, Anshu Aviral, Priyatham Bollimpalli
In this work, we propose a novel approach that predicts the relationships between various entities in an image in a weakly supervised manner by relying on image captions and object bounding box annotations as the sole source of supervision.
no code implementations • NAACL 2021 • Deepak Muralidharan, Joel Ruben Antony Moniz, Sida Gao, Xiao Yang, Justine Kao, Stephen Pulman, Atish Kothari, Ray Shen, Yinying Pan, Vivek Kaul, Mubarak Seyed Ibrahim, Gang Xiang, Nan Dun, Yidan Zhou, Andy O, Yuan Zhang, Pooja Chitkara, Xuan Wang, Alkesh Patel, Kushal Tayal, Roger Zheng, Peter Grasch, Jason D. Williams, Lin Li
Named Entity Recognition (NER) and Entity Linking (EL) play an essential role in voice assistant interaction, but are challenging due to the special difficulties associated with spoken user queries.
no code implementations • 15 Aug 2021 • Deepak Muralidharan, Joel Ruben Antony Moniz, Weicheng Zhang, Stephen Pulman, Lin Li, Megan Barnes, Jingjing Pan, Jason Williams, Alex Acero
Named entity recognition (NER) is usually developed and tested on text from well-written sources.
no code implementations • EMNLP (NLP4ConvAI) 2021 • Sahas Dendukuri, Pooja Chitkara, Joel Ruben Antony Moniz, Xiao Yang, Manos Tsagkias, Stephen Pulman
Entity tags in human-machine dialog are integral to natural language understanding (NLU) tasks in conversational assistants.
no code implementations • 13 Oct 2021 • Alkesh Patel, Joel Ruben Antony Moniz, Roman Nguyen, Nick Tzou, Hadas Kotek, Vincent Renkens
In multimodal assistant, where vision is also one of the input modalities, the identification of user intent becomes a challenging task as visual input can influence the outcome.
no code implementations • 27 Sep 2018 • Barun Patra, Joel Ruben Antony Moniz, Sarthak Garg, Matthew R Gormley, Graham Neubig
We then propose Bilingual Lexicon Induction with Semi-Supervision (BLISS) --- a novel semi-supervised approach that relaxes the isometric assumption while leveraging both limited aligned bilingual lexicons and a larger set of unaligned word embeddings, as well as a novel hubness filtering technique.
no code implementations • 2 Jun 2023 • Jiarui Lu, Bo-Hsiang Tseng, Joel Ruben Antony Moniz, Site Li, Xueyun Zhu, Hong Yu, Murat Akbacak
Providing voice assistants the ability to navigate multi-turn conversations is a challenging problem.
no code implementations • 7 Aug 2023 • Cecilia Aas, Hisham Abdelsalam, Irina Belousova, Shruti Bhargava, Jianpeng Cheng, Robert Daland, Joris Driesen, Federico Flego, Tristan Guigue, Anders Johannsen, Partha Lal, Jiarui Lu, Joel Ruben Antony Moniz, Nathan Perkins, Dhivya Piraviperumal, Stephen Pulman, Diarmuid Ó Séaghdha, David Q. Sun, John Torr, Marco Del Vecchio, Jay Wacker, Jason D. Williams, Hong Yu
It has recently become feasible to run personal digital assistants on phones and other personal devices.
no code implementations • 25 Oct 2023 • Leon Liyang Zhang, Jiarui Lu, Joel Ruben Antony Moniz, Aditya Kulkarni, Dhivya Piraviperumal, Tien Dung Tran, Nicholas Tzou, Hong Yu
In the context of a voice assistant system, steering refers to the phenomenon in which a user issues a follow-up command attempting to direct or clarify a previous turn.
no code implementations • 3 Nov 2023 • Halim Cagri Ates, Shruti Bhargava, Site Li, Jiarui Lu, Siddhardha Maddula, Joel Ruben Antony Moniz, Anil Kumar Nalamalapu, Roman Hoang Nguyen, Melis Ozyildirim, Alkesh Patel, Dhivya Piraviperumal, Vincent Renkens, Ankit Samal, Thy Tran, Bo-Hsiang Tseng, Hong Yu, Yuan Zhang, Rong Zou
Successfully handling context is essential for any dialog understanding task.
no code implementations • 1 Feb 2024 • YIlun Zhu, Joel Ruben Antony Moniz, Shruti Bhargava, Jiarui Lu, Dhivya Piraviperumal, Site Li, Yuan Zhang, Hong Yu, Bo-Hsiang Tseng
Understanding context is key to understanding human language, an ability which Large Language Models (LLMs) have been increasingly seen to demonstrate to an impressive extent.
no code implementations • 3 Feb 2024 • Atharva Kulkarni, Bo-Hsiang Tseng, Joel Ruben Antony Moniz, Dhivya Piraviperumal, Hong Yu, Shruti Bhargava
Remarkably, our few-shot learning approach recovers nearly $98%$ of the performance compared to the few-shot setup using human-annotated training data.
no code implementations • 29 Mar 2024 • Joel Ruben Antony Moniz, Soundarya Krishnan, Melis Ozyildirim, Prathamesh Saraf, Halim Cagri Ates, Yuan Zhang, Hong Yu, Nidhi Rajshree
Reference resolution is an important problem, one that is essential to understand and successfully handle context of different kinds.
1 code implementation • ACL 2022 • Joel Ruben Antony Moniz, Barun Patra, Matthew R. Gormley
When tasked with supporting multiple languages for a given problem, two approaches have arisen: training a model for each language with the annotation budget divided equally among them, and training on a high-resource language followed by zero-shot transfer to the remaining languages.
1 code implementation • ACL 2019 • Barun Patra, Joel Ruben Antony Moniz, Sarthak Garg, Matthew R. Gormley, Graham Neubig
We then propose Bilingual Lexicon Induction with Semi-Supervision (BLISS) --- a semi-supervised approach that relaxes the isometric assumption while leveraging both limited aligned bilingual lexicons and a larger set of unaligned word embeddings, as well as a novel hubness filtering technique.
1 code implementation • NAACL 2021 • Bo-Hsiang Tseng, Shruti Bhargava, Jiarui Lu, Joel Ruben Antony Moniz, Dhivya Piraviperumal, Lin Li, Hong Yu
In this work, we propose a novel joint learning framework of modeling coreference resolution and query rewriting for complex, multi-turn dialogue understanding.
1 code implementation • NeurIPS 2018 • Joel Ruben Antony Moniz, Christopher Beckham, Simon Rajotte, Sina Honari, Christopher Pal
We present an unsupervised approach for learning to estimate three dimensional (3D) facial structure from a single image while also predicting 3D viewpoint transformations that match a desired pose and facial geometry.
1 code implementation • 31 Jan 2018 • Joel Ruben Antony Moniz, David Krueger
We propose Nested LSTMs (NLSTM), a novel RNN architecture with multiple levels of memory.