Papers by Qiuyuan Huang
NICE: Neural Image Commenting with Empathy (2021.findings-emnlp)
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Kezhen Chen, Qiuyuan Huang, Daniel McDuff, Xiang Gao, Hamid Palangi, Jianfeng Wang, Kenneth Forbus, Jianfeng Gao
| Challenge: | Emotion and empathy are examples of human qualities lacking in many human-machine interactions. |
| Approach: | They propose to generate images with human-generated comments with enhanced emotion and empathy while minimizing inappropriate or offensive outputs. |
| Outcome: | The proposed model generates more human-like and engaging image comments on two images with human-generated comments and human annotations while minimizing inappropriate or offensive outputs. |
Tensor Product Generation Networks for Deep NLP Modeling (N18-1)
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| Challenge: | Using Tensor Product Representations (TPRs) we propose a new architecture for natural language processing based on the principle that hypothesis space for learning includes network hypotheses that are independently known to be suitable for performing the target task. |
| Approach: | They propose a Tensor Product Generation Network (TPGN) which is capable of carrying out TPR computation but uses unconstrained deep learning to design its internal representations. |
| Outcome: | The proposed architecture outperforms baselines on the COCO dataset and can interpret internal representations and operations. |
KAT: A Knowledge Augmented Transformer for Vision-and-Language (2022.naacl-main)
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| Challenge: | Existing methods for knowledge retrieval and answer prediction have left open questions about the quality and relevance of the retrieved knowledge and how the reasoning processes over implicit and explicit knowledge should be integrated. |
| Approach: | They propose a Knowledge Augmented Transformer which integrates both implicit and explicit knowledge in an encoder-decoder architecture while simultaneously reasoning over both knowledge sources during answer generation. |
| Outcome: | The proposed model achieves a strong state-of-the-art (+6% absolute) on the open-domain multimodal task of OK-VQA. |
MindAgent: Emergent Gaming Interaction (2024.findings-naacl)
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Ran Gong, Qiuyuan Huang, Xiaojian Ma, Yusuke Noda, Zane Durante, Zilong Zheng, Demetri Terzopoulos, Li Fei-Fei, Jianfeng Gao, Hoi Vo
| Challenge: | Large foundation models (LFMs) can perform complex scheduling in a multi-agent system and can coordinate agents to complete complex tasks that require extensive collaboration. |
| Approach: | They propose a gaming-based infrastructure that evaluates LFMs' planning and coordination capabilities in the context of gaming interaction. |
| Outcome: | The proposed infrastructure can be deployed in a customized VR version of Cuisineworld and adapted in the “Minecraft” domain. |
Logical Transformers: Infusing Logical Structures into Pre-Trained Language Models (2023.findings-acl)
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Borui Wang, Qiuyuan Huang, Budhaditya Deb, Aaron Halfaker, Liqun Shao, Daniel McDuff, Ahmed Hassan Awadallah, Dragomir Radev, Jianfeng Gao
| Challenge: | Existing pre-trained language models that ignore the logical structures underlying natural language text often lack the ability to capture and encode key logical information in the input sequences. |
| Approach: | They propose to construct logic-aware input embeddings for transformer language models through logic detection, logic mapping and hierarchical logical projections and then develop a new modeling paradigm that can upgrade existing transformer language model into logical transformers to boost their performance. |
| Outcome: | The proposed model can achieve superior performance on four important and challenging tasks. |
TIGEr: Text-to-Image Grounding for Image Caption Evaluation (D19-1)
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Ming Jiang, Qiuyuan Huang, Lei Zhang, Xin Wang, Pengchuan Zhang, Zhe Gan, Jana Diesner, Jianfeng Gao
| Challenge: | Existing metrics based on text-level comparisons fail to assess the quality of captions produced by machines. |
| Approach: | They propose to use a machine-learned text-image grounding model to measure the accuracy of machine-generated captions and their correlation with human judgments. |
| Outcome: | The proposed metric has higher consistency with human judgments and is more accurate than existing metrics. |
REO-Relevance, Extraness, Omission: A Fine-grained Evaluation for Image Captioning (D19-1)
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| Challenge: | Existing metrics for image captioning evaluation provide an overall quality score, which is difficult to infer specific description errors. |
| Approach: | They propose a fine-grained evaluation method REO for automatically measuring the performance of image captioning systems. |
| Outcome: | The proposed method achieves higher consistency with human judgments and provides more intuitive evaluation results than other metrics. |