no code implementations • 24 May 2024 • Run Luo, Yunshui Li, Longze Chen, Wanwei He, Ting-En Lin, Ziqiang Liu, Lei Zhang, Zikai Song, Xiaobo Xia, Tongliang Liu, Min Yang, Binyuan Hui
Delving into the modeling capabilities of diffusion models for images naturally prompts the question: Can diffusion models serve as the eyes of large language models for image perception?
1 code implementation • 22 Apr 2024 • Zhengwei Tao, Ting-En Lin, Xiancai Chen, Hangyu Li, Yuchuan Wu, Yongbin Li, Zhi Jin, Fei Huang, DaCheng Tao, Jingren Zhou
Large language models (LLMs) have significantly advanced in various fields and intelligent agent applications.
1 code implementation • 4 Mar 2024 • Changyu Chen, Xiting Wang, Ting-En Lin, Ang Lv, Yuchuan Wu, Xin Gao, Ji-Rong Wen, Rui Yan, Yongbin Li
In reasoning tasks, even a minor error can cascade into inaccurate results, leading to suboptimal performance of large language models in such domains.
1 code implementation • 7 Dec 2023 • Yuhan Chen, Ang Lv, Ting-En Lin, Changyu Chen, Yuchuan Wu, Fei Huang, Yongbin Li, Rui Yan
Specifically, the crucial information in the context will be potentially overlooked by model when it is positioned in the trough zone of the attention waveform, leading to decreased performance.
Ranked #2 on Trajectory Planning on ToolBench
no code implementations • 30 Oct 2023 • Huawen Feng, Yan Fan, Xiong Liu, Ting-En Lin, Zekun Yao, Yuchuan Wu, Fei Huang, Yongbin Li, Qianli Ma
Despite the recent progress in text summarization made by large language models (LLMs), they often generate summaries that are factually inconsistent with original articles, known as "hallucinations" in text generation.
no code implementations • 10 Oct 2023 • Tianshu Yu, Ting-En Lin, Yuchuan Wu, Min Yang, Fei Huang, Yongbin Li
This limitation leads to suboptimal performance, even when ample training data is available.
no code implementations • 22 Sep 2023 • Haoyu Gao, Ting-En Lin, Hangyu Li, Min Yang, Yuchuan Wu, Wentao Ma, Yongbin Li
Task-oriented dialogue (TOD) systems facilitate users in executing various activities via multi-turn dialogues, but Large Language Models (LLMs) often struggle to comprehend these intricate contexts.
2 code implementations • 4 Sep 2023 • Zaijing Li, Ting-En Lin, Yuchuan Wu, Meng Liu, Fengxiao Tang, Ming Zhao, Yongbin Li
Sentiment analysis is a crucial task that aims to understand people's emotional states and predict emotional categories based on multimodal information.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2
no code implementations • NeurIPS 2023 • Shuzheng Si, Wentao Ma, Haoyu Gao, Yuchuan Wu, Ting-En Lin, Yinpei Dai, Hangyu Li, Rui Yan, Fei Huang, Yongbin Li
SpokenWOZ further incorporates common spoken characteristics such as word-by-word processing and reasoning in spoken language.
1 code implementation • 19 May 2023 • Tianshu Yu, Haoyu Gao, Ting-En Lin, Min Yang, Yuchuan Wu, Wentao Ma, Chao Wang, Fei Huang, Yongbin Li
In this paper, we propose Speech-text dialog Pre-training for spoken dialog understanding with ExpliCiT cRoss-Modal Alignment (SPECTRA), which is the first-ever speech-text dialog pre-training model.
Ranked #1 on Multimodal Sentiment Analysis on MOSI
Emotion Recognition in Conversation Multimodal Intent Recognition +1
1 code implementation • 4 May 2023 • Haoyu Gao, Rui Wang, Ting-En Lin, Yuchuan Wu, Min Yang, Fei Huang, Yongbin Li
Dialogue Topic Segmentation (DTS) plays an essential role in a variety of dialogue modeling tasks.
no code implementations • 23 Feb 2023 • Yushan Qian, Bo wang, Ting-En Lin, Yinhe Zheng, Ying Zhu, Dongming Zhao, Yuexian Hou, Yuchuan Wu, Yongbin Li
Empathetic dialogue is a human-like behavior that requires the perception of both affective factors (e. g., emotion status) and cognitive factors (e. g., cause of the emotion).
1 code implementation • ICCV 2023 • Ruoyi Du, Wenqing Yu, Heqing Wang, Ting-En Lin, Dongliang Chang, Zhanyu Ma
Despite the remarkable progress of Fine-grained visual classification (FGVC) with years of history, it is still limited to recognizing 2 images.
Fine-Grained Image Classification Fine-Grained Visual Recognition
1 code implementation • 21 Nov 2022 • Guimin Hu, Ting-En Lin, Yi Zhao, Guangming Lu, Yuchuan Wu, Yongbin Li
Multimodal sentiment analysis (MSA) and emotion recognition in conversation (ERC) are key research topics for computers to understand human behaviors.
Ranked #2 on Multimodal Sentiment Analysis on CMU-MOSI
1 code implementation • 2 Jun 2022 • Ruoyi Du, Wenqing Yu, Heqing Wang, Dongliang Chang, Ting-En Lin, Yongbin Li, Zhanyu Ma
As fine-grained visual classification (FGVC) being developed for decades, great works related have exposed a key direction -- finding discriminative local regions and revealing subtle differences.
no code implementations • 30 May 2022 • Ting-En Lin, Yuchuan Wu, Fei Huang, Luo Si, Jian Sun, Yongbin Li
In this paper, we present Duplex Conversation, a multi-turn, multimodal spoken dialogue system that enables telephone-based agents to interact with customers like a human.
1 code implementation • 18 Dec 2020 • Hanlei Zhang, Hua Xu, Ting-En Lin
In this paper, we propose a post-processing method to learn the adaptive decision boundary (ADB) for open intent classification.
Ranked #1 on Open Intent Detection on StackOverFlow(75%known)
2 code implementations • 16 Dec 2020 • Hanlei Zhang, Hua Xu, Ting-En Lin, Rui Lyu
In this work, we propose an effective method, Deep Aligned Clustering, to discover new intents with the aid of the limited known intent data.
Ranked #2 on Open Intent Discovery on CLINC150
1 code implementation • 7 Mar 2020 • Ting-En Lin, Hua Xu
In this paper, we propose SofterMax and deep novelty detection (SMDN), a simple yet effective post-processing method for detecting unknown intent in dialogue systems based on pre-trained deep neural network classifiers.
1 code implementation • 20 Nov 2019 • Ting-En Lin, Hua Xu, Hanlei Zhang
Identifying new user intents is an essential task in the dialogue system.
Ranked #1 on Open Intent Discovery on ATIS
1 code implementation • ACL 2019 • Ting-En Lin, Hua Xu
With margin loss, we can learn discriminative deep features by forcing the network to maximize inter-class variance and to minimize intra-class variance.
Ranked #1 on Open Intent Detection on SNIPS (25% known)