no code implementations • 21 May 2022 • Abdelrahman Mohamed, Hung-Yi Lee, Lasse Borgholt, Jakob D. Havtorn, Joakim Edin, Christian Igel, Katrin Kirchhoff, Shang-Wen Li, Karen Livescu, Lars Maaløe, Tara N. Sainath, Shinji Watanabe
Although self-supervised speech representation is still a nascent research area, it is closely related to acoustic word embedding and learning with zero lexical resources, both of which have seen active research for many years.
no code implementations • 3 May 2022 • Hung-Yi Lee, Shang-Wen Li, Ngoc Thang Vu
Deep learning has been the mainstream technique in natural language processing (NLP) area.
1 code implementation • 21 Apr 2022 • Yung-Sung Chuang, Rumen Dangovski, Hongyin Luo, Yang Zhang, Shiyu Chang, Marin Soljačić, Shang-Wen Li, Wen-tau Yih, Yoon Kim, James Glass
We propose DiffCSE, an unsupervised contrastive learning framework for learning sentence embeddings.
Ranked #7 on
Semantic Textual Similarity
on STS16
1 code implementation • 31 Mar 2022 • Kai-Wei Chang, Wei-Cheng Tseng, Shang-Wen Li, Hung-Yi Lee
We report in this paper the first exploration of the prompt tuning paradigm for speech processing tasks based on Generative Spoken Language Model (GSLM).
1 code implementation • 27 Mar 2022 • Guan-Ting Lin, Shang-Wen Li, Hung-Yi Lee
Although deep learning-based end-to-end Automatic Speech Recognition (ASR) has shown remarkable performance in recent years, it suffers severe performance regression on test samples drawn from different data distributions.
1 code implementation • ACL 2022 • Hsiang-Sheng Tsai, Heng-Jui Chang, Wen-Chin Huang, Zili Huang, Kushal Lakhotia, Shu-wen Yang, Shuyan Dong, Andy T. Liu, Cheng-I Jeff Lai, Jiatong Shi, Xuankai Chang, Phil Hall, Hsuan-Jui Chen, Shang-Wen Li, Shinji Watanabe, Abdelrahman Mohamed, Hung-Yi Lee
In this paper, we introduce SUPERB-SG, a new benchmark focused on evaluating the semantic and generative capabilities of pre-trained models by increasing task diversity and difficulty over SUPERB.
1 code implementation • 9 Mar 2022 • Guan-Ting Lin, Yung-Sung Chuang, Ho-Lam Chung, Shu-wen Yang, Hsuan-Jui Chen, Shuyan Dong, Shang-Wen Li, Abdelrahman Mohamed, Hung-Yi Lee, Lin-shan Lee
We empirically showed that DUAL yields results comparable to those obtained by cascading ASR and text QA model and robust to real-world data.
no code implementations • 3 Mar 2022 • Andy T. Liu, Wei Xiao, Henghui Zhu, Dejiao Zhang, Shang-Wen Li, Andrew Arnold
Recently, prompt-based learning for pre-trained language models has succeeded in few-shot Named Entity Recognition (NER) by exploiting prompts as task guidance to increase label efficiency.
no code implementations • BigScience (ACL) 2022 • Xisen Jin, Dejiao Zhang, Henghui Zhu, Wei Xiao, Shang-Wen Li, Xiaokai Wei, Andrew Arnold, Xiang Ren
We evaluate PTLM's ability to adapt to new corpora while retaining learned knowledge in earlier corpora.
1 code implementation • EMNLP 2021 • Dejiao Zhang, Shang-Wen Li, Wei Xiao, Henghui Zhu, Ramesh Nallapati, Andrew O. Arnold, Bing Xiang
Many recent successes in sentence representation learning have been achieved by simply fine-tuning on the Natural Language Inference (NLI) datasets with triplet loss or siamese loss.
no code implementations • ACL 2021 • Hung-Yi Lee, Ngoc Thang Vu, Shang-Wen Li
Meta-learning is one of the most important new techniques in machine learning in recent years.
1 code implementation • ACL (WOAH) 2021 • Yung-Sung Chuang, Mingye Gao, Hongyin Luo, James Glass, Hung-Yi Lee, Yun-Nung Chen, Shang-Wen Li
Automatic detection of toxic language plays an essential role in protecting social media users, especially minority groups, from verbal abuse.
no code implementations • 6 Jun 2021 • Hongyin Luo, Shuyan Dong, Yung-Sung Chuang, Shang-Wen Li
Neural network pretraining is gaining attention due to its outstanding performance in natural language processing applications.
3 code implementations • 3 May 2021 • Shu-wen Yang, Po-Han Chi, Yung-Sung Chuang, Cheng-I Jeff Lai, Kushal Lakhotia, Yist Y. Lin, Andy T. Liu, Jiatong Shi, Xuankai Chang, Guan-Ting Lin, Tzu-Hsien Huang, Wei-Cheng Tseng, Ko-tik Lee, Da-Rong Liu, Zili Huang, Shuyan Dong, Shang-Wen Li, Shinji Watanabe, Abdelrahman Mohamed, Hung-Yi Lee
SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data.
no code implementations • 12 Mar 2021 • Hongyin Luo, Shang-Wen Li, Seunghak Yu, James Glass
REGEX is built upon a masked answer extraction task with an interactive learning environment containing an answer entity REcognizer, a question Generator, and an answer EXtractor.
no code implementations • EMNLP (ClinicalNLP) 2020 • Hongyin Luo, Shang-Wen Li, James Glass
Given a set of explicit symptoms provided by the patient to initiate a dialog for diagnosing, the system is trained to collect implicit symptoms by asking questions, in order to collect more information for making an accurate diagnosis.
no code implementations • EACL 2021 • Shuyang Li, Jin Cao, Mukund Sridhar, Henghui Zhu, Shang-Wen Li, Wael Hamza, Julian McAuley
Dialog State Tracking (DST), an integral part of modern dialog systems, aims to track user preferences and constraints (slots) in task-oriented dialogs.
no code implementations • 31 Dec 2020 • Shang-Wen Li
By linking and organizing pieces of learning content scattered in various course materials into an easily accessible structure, we hypothesize that this framework can provide learners guidance and improve content navigation.
no code implementations • 30 Nov 2020 • Shang-Wen Li, Jason Krone, Shuyan Dong, Yi Zhang, Yaser Al-Onaizan
Recently deep learning has dominated many machine learning areas, including spoken language understanding (SLU).
no code implementations • 11 Nov 2020 • Cheng-I Lai, Jin Cao, Sravan Bodapati, Shang-Wen Li
Much recent work on Spoken Language Understanding (SLU) falls short in at least one of three ways: models were trained on oracle text input and neglected the Automatics Speech Recognition (ASR) outputs, models were trained to predict only intents without the slot values, or models were trained on a large amount of in-house data.
1 code implementation • 26 Oct 2020 • Cheng-I Lai, Yung-Sung Chuang, Hung-Yi Lee, Shang-Wen Li, James Glass
Much recent work on Spoken Language Understanding (SLU) is limited in at least one of three ways: models were trained on oracle text input and neglected ASR errors, models were trained to predict only intents without the slot values, or models were trained on a large amount of in-house data.
no code implementations • 9 Oct 2020 • Jin Cao, Jun Wang, Wael Hamza, Kelly Vanee, Shang-Wen Li
The light encoder architecture separates the shared pre-trained networks from the mappings of generally encoded knowledge to specific domains of SLU, allowing for the domain adaptation to be performed solely at the light encoder and thus increasing efficiency.
6 code implementations • 12 Jul 2020 • Andy T. Liu, Shang-Wen Li, Hung-Yi Lee
We present a large-scale comparison of various self-supervised models.
no code implementations • 19 May 2020 • Hongyin Luo, Shang-Wen Li, James Glass
Experiments showed that the ProtoQN significantly outperformed the baseline DQN model in both supervised and few-shot learning scenarios, and achieves state-of-the-art few-shot learning performances.
3 code implementations • 18 May 2020 • Po-Han Chi, Pei-Hung Chung, Tsung-Han Wu, Chun-Cheng Hsieh, Yen-Hao Chen, Shang-Wen Li, Hung-Yi Lee
We use the representations with two downstream tasks, speaker identification, and phoneme classification.
no code implementations • 11 Dec 2017 • Maryam Fazel-Zarandi, Shang-Wen Li, Jin Cao, Jared Casale, Peter Henderson, David Whitney, Alborz Geramifard
In this paper, we focus on learning robust dialog policies to recover from these errors.
Automatic Speech Recognition
Natural Language Understanding
+1
1 code implementation • 3 Jul 2016 • Yuzhuo Ren, Chen Chen, Shang-Wen Li, C. -C. Jay Kuo
The task of estimating the spatial layout of cluttered indoor scenes from a single RGB image is addressed in this work.
no code implementations • 3 Apr 2016 • Yuzhuo Ren, Chen Chen, Shang-Wen Li, C. -C. Jay Kuo
The proposed Global-attributes Assisted Labeling (GAL) system exploits both local features and global attributes.
no code implementations • 28 Feb 2016 • Shang-Wen Li, Sanjay Purushotham, Chen Chen, Yuzhuo Ren, C. -C. Jay Kuo
Textual data such as tags, sentence descriptions are combined with visual cues to reduce the semantic gap for image retrieval applications in today's Multimodal Image Retrieval (MIR) systems.