Papers by Hexiang Hu
Learning to Represent Image and Text with Denotation Graph (2020.emnlp-main)
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| Challenge: | Recent advances in learning representations of visual and language information have been a problem with many applications. |
| Approach: | They propose to extract visual expressions from images aligned with linguistic expressions that describe the images to learn representations from implicit expressions. |
| Outcome: | The proposed representations lead to stronger empirical results on downstream tasks of cross-modal image retrieval, referring expression, and compositional attribute-object recognition. |
MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text (2022.emnlp-main)
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| Challenge: | Pre-trained language models store a massive amount of world knowledge implicitly in their parameters, but large models often fail to encode information about rare entities and events. |
| Approach: | They propose a retrieval-augmented model which accesses an external non-parametric memory to augment language generation. |
| Outcome: | The proposed model outperforms existing models by 10-20% absolute on two datasets and under distractor and full-wiki settings. |
Multi-Task Learning for Sequence Tagging: An Empirical Study (C18-1)
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| Challenge: | Existing work on "pairwise" MTL has been validated in sequence tagging but key issues remain about its effectiveness. |
| Approach: | They propose three general multi-task learning approaches on 11 sequence tagging tasks. |
| Outcome: | The proposed approaches improve on 11 sequence tagging tasks. |
Systematic Generalization on gSCAN: What is Nearly Solved and What is Next? (2021.emnlp-main)
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| Challenge: | a general-purpose Transformer-based model with crossmodal attention solves most of the systematic generalization problems . current models are data inefficient given the narrow scope of commands in gSCAN . |
| Approach: | They propose to use a Transformer-based model with cross-modal attention to solve gSCAN . they propose to generate data to incorporate relations between objects in the visual environment . |
| Outcome: | The proposed model outperforms specialized approaches on most splits, and is data inefficient given the narrow scope of commands. |
BabyWalk: Going Farther in Vision-and-Language Navigation by Taking Baby Steps (2020.acl-main)
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| Challenge: | Existing state-of-the-art VLN agents do not generalize well for long navigation tasks. |
| Approach: | They propose a VLN agent that is learned to navigate by decomposing long instructions into shorter ones and completing them sequentially. |
| Outcome: | The proposed agent can follow long instructions better than existing ones, but it does not generalize well. |
LOFT: Scalable and More Realistic Long-Context Evaluation (2025.findings-naacl)
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Jinhyuk Lee, Anthony Chen, Zhuyun Dai, Dheeru Dua, Devendra Singh Sachan, Michael Boratko, Yi Luan, Séb Arnold, Vincent Perot, Siddharth Dalmia, Hexiang Hu, Xudong Lin, Panupong Pasupat, Aida Amini, Jeremy R. Cole, Sebastian Riedel, Iftekhar Naim, Ming-Wei Chang, Kelvin Guu
| Challenge: | Long-context language models (LCLMs) can be used to perform tasks traditionally reliant on external tools like retrieval systems or databases. |
| Approach: | They propose a benchmark to evaluate LCLMs' performance on in-context retrieval and reasoning tasks using a set of tokens. |
| Outcome: | The proposed model outperforms state-of-the-art retrieval and RAG systems on in-context retrieval tasks while still requiring prompting strategies. |
Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets (N18-1)
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| Challenge: | Visual question answering datasets are a form of (visual) Turing test that artificial intelligence should strive to achieve. |
| Approach: | They propose automatic procedures to remedy design deficiencies in visual question answering datasets . they propose to use a set of decoys to re-construct decoying answers for two popular Visual QA datasets. |
| Outcome: | The proposed procedures improve the performance of the proposed datasets. |
Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions? (2023.emnlp-main)
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| Challenge: | Pre-trained vision and language models have demonstrated state-of-the-art capabilities over existing tasks involving images and texts. |
| Approach: | They analyze a visual question answering dataset tailored for info-seeking questions . they show that pre-trained visual and language models can use fine-grained knowledge . |
| Outcome: | The proposed dataset elicits models to use fine-grained knowledge learned during pre-training. |
Visually Grounded Concept Composition (2021.findings-emnlp)
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| Challenge: | Existing approaches to visual grounding do not explicitly model compositional structures of text expressions. |
| Approach: | They propose a concept-relation Graph and a composition neural network to combine CRGs . they propose to align CRG-based concepts with images to learn visually grounded concepts . |
| Outcome: | The proposed model can model grounded concepts forming at sentence level and word level. |