1 code implementation • MTSummit 2021 • William Chen, Brett Fazio
Neural Machine Translation (NMT) for Low Resource Languages (LRL) is often limited by the lack of available training data, making it necessary to explore additional techniques to improve translation quality.
no code implementations • MTSummit 2021 • William Chen, Brett Fazio
We present the University of Central Florida systems for the LoResMT 2021 Shared Task, participating in the English-Irish and English-Marathi translation pairs.
no code implementations • 5 Feb 2024 • William Chen, Oier Mees, Aviral Kumar, Sergey Levine
We find that our policies trained on embeddings extracted from general-purpose VLMs outperform equivalent policies trained on generic, non-promptable image embeddings.
no code implementations • 30 Jan 2024 • Yifan Peng, Jinchuan Tian, William Chen, Siddhant Arora, Brian Yan, Yui Sudo, Muhammad Shakeel, Kwanghee Choi, Jiatong Shi, Xuankai Chang, Jee-weon Jung, Shinji Watanabe
In this work, we aim to improve the performance and efficiency of OWSM without extra training data.
1 code implementation • 10 Jan 2024 • Jee-weon Jung, Roshan Sharma, William Chen, Bhiksha Raj, Shinji Watanabe
We tackle this challenge by proposing AugSumm, a method to leverage large language models (LLMs) as a proxy for human annotators to generate augmented summaries for training and evaluation.
no code implementations • 18 Dec 2023 • Jared Strader, Nathan Hughes, William Chen, Alberto Speranzon, Luca Carlone
This paper proposes an approach to build 3D scene graphs in arbitrary (indoor and outdoor) environments.
no code implementations • 9 Oct 2023 • Jiatong Shi, William Chen, Dan Berrebbi, Hsiu-Hsuan Wang, Wei-Ping Huang, En-Pei Hu, Ho-Lam Chuang, Xuankai Chang, Yuxun Tang, Shang-Wen Li, Abdelrahman Mohamed, Hung-Yi Lee, Shinji Watanabe
The 2023 Multilingual Speech Universal Performance Benchmark (ML-SUPERB) Challenge expands upon the acclaimed SUPERB framework, emphasizing self-supervised models in multilingual speech recognition and language identification.
no code implementations • 5 Oct 2023 • Chih-Chen Chen, William Chen, Rodolfo Zevallos, John E. Ortega
The application of self-supervision to speech representation learning has garnered significant interest in recent years, due to its scalability to large amounts of unlabeled data.
no code implementations • 26 Sep 2023 • William Chen, Jiatong Shi, Brian Yan, Dan Berrebbi, Wangyou Zhang, Yifan Peng, Xuankai Chang, Soumi Maiti, Shinji Watanabe
We show that further efficiency can be achieved with a vanilla HuBERT Base model, which can maintain 94% of XLS-R's performance with only 3% of the data, 4 GPUs, and limited trials.
1 code implementation • 25 Sep 2023 • Yifan Peng, Jinchuan Tian, Brian Yan, Dan Berrebbi, Xuankai Chang, Xinjian Li, Jiatong Shi, Siddhant Arora, William Chen, Roshan Sharma, Wangyou Zhang, Yui Sudo, Muhammad Shakeel, Jee-weon Jung, Soumi Maiti, Shinji Watanabe
Pre-training speech models on large volumes of data has achieved remarkable success.
no code implementations • 11 Jun 2023 • William Chen, Xuankai Chang, Yifan Peng, Zhaoheng Ni, Soumi Maiti, Shinji Watanabe
Our code and training optimizations make SSL feasible with only 8 GPUs, instead of the 32 used in the original work.
2 code implementations • 19 May 2023 • Jiyang Tang, William Chen, Xuankai Chang, Shinji Watanabe, Brian MacWhinney
Our system achieves state-of-the-art speaker-level detection accuracy (97. 3%), and a relative WER reduction of 11% for moderate Aphasia patients.
2 code implementations • 18 May 2023 • Yifan Peng, Kwangyoun Kim, Felix Wu, Brian Yan, Siddhant Arora, William Chen, Jiyang Tang, Suwon Shon, Prashant Sridhar, Shinji Watanabe
Conformer, a convolution-augmented Transformer variant, has become the de facto encoder architecture for speech processing due to its superior performance in various tasks, including automatic speech recognition (ASR), speech translation (ST) and spoken language understanding (SLU).
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 18 May 2023 • Jiatong Shi, Dan Berrebbi, William Chen, Ho-Lam Chung, En-Pei Hu, Wei Ping Huang, Xuankai Chang, Shang-Wen Li, Abdelrahman Mohamed, Hung-Yi Lee, Shinji Watanabe
Speech processing Universal PERformance Benchmark (SUPERB) is a leaderboard to benchmark the performance of Self-Supervised Learning (SSL) models on various speech processing tasks.
1 code implementation • 24 Feb 2023 • William Chen, Brian Yan, Jiatong Shi, Yifan Peng, Soumi Maiti, Shinji Watanabe
In this paper, we introduce our work on improving performance on FLEURS, a 102-language open ASR benchmark, by conditioning the entire model on language identity (LID).
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 3 Feb 2023 • Belinda Z. Li, William Chen, Pratyusha Sharma, Jacob Andreas
Language models trained on large text corpora encode rich distributional information about real-world environments and action sequences.
1 code implementation • 12 Sep 2022 • William Chen, Siyi Hu, Rajat Talak, Luca Carlone
Abstract semantic 3D scene understanding is a problem of critical importance in robotics.
no code implementations • 29 Jul 2022 • Chih-Chen Chen, William Chen
Little research has been done on Neural Machine Translation (NMT) for Azerbaijani.
no code implementations • 9 Jun 2022 • William Chen, Siyi Hu, Rajat Talak, Luca Carlone
Semantic 3D scene understanding is a problem of critical importance in robotics.
no code implementations • 5 May 2021 • William Chen, Kensal Ramos, Kalyan Naidu Mullaguri, Annie S. Wu
Most current work in NLP utilizes deep learning, which requires a lot of training data and computational power.
no code implementations • 17 Dec 2020 • Jozsef Beck, William Chen
Given any rectangular polyhedron 3-manifold $P$ tiled with unit cubes, we find infinitely many explicit directions related to cubic algebraic numbers such that all half-infinite geodesics in these directions are uniformly distributed in $P$.
Number Theory 11K38, 37E35
no code implementations • 11 Nov 2020 • Chung Hoon Hong, Yuan Liang, Sagnik Sinha Roy, Arushi Jain, Vihang Agarwal, Ryan Draves, Zhizhuo Zhou, William Chen, Yujian Liu, Martha Miracky, Lily Ge, Nikola Banovic, David Jurgens
Conversational Intelligence requires that a person engage on informational, personal and relational levels.
no code implementations • NeurIPS 2013 • Hossein Azari Soufiani, William Chen, David C. Parkes, Lirong Xia
In this paper we propose a class of efficient Generalized Method-of-Moments(GMM) algorithms for computing parameters of the Plackett-Luce model, where the data consists of full rankings over alternatives.