Papers by Jaemin Cho
A Hierarchical Latent Structure for Variational Conversation Modeling (N18-1)
Copied to clipboard
| Challenge: | Variational autoencoders suffer from the notorious degeneration problem, according to a new study . utterance drop regularization is an important feature of the hierarchical RNNs . |
| Approach: | They propose a variational hierarchical conversation RNN framework that exploits latent variables and an utterance drop regularization to exploit latent variable. |
| Outcome: | The proposed model outperforms state-of-the-art models on Cornell Movie Dialog and Ubuntu Dialog Corpus. |
X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers (2020.emnlp-main)
Copied to clipboard
| Challenge: | Recent work has adapted vision-and-language models to generative tasks like image captioning. |
| Approach: | They propose an extension to LXMERT with training refinements to generate images from text. |
| Outcome: | The proposed model can generate images from pieces of text while still being comparable to existing models. |
Mixture Content Selection for Diverse Sequence Generation (D19-1)
Copied to clipboard
| Challenge: | Generating diverse sequences exhibit semantically one-to-many relationships between source and target sequences. |
| Approach: | They propose to separate diversification from generation using a general plug-and-play module that wraps around and guides an existing encoder-decoder model. |
| Outcome: | The proposed method shows that diversification and generation are separate steps in the same model and that the model is robust. |
Disentangling Structure and Style: Political Bias Detection in News by Inducing Document Hierarchy (2023.findings-emnlp)
Copied to clipboard
| Challenge: | a new method to detect political bias in news articles overcomes this domain dependency . partisan bias exists in various social issues, including the 2016 presidential election . |
| Approach: | They propose a multi-head hierarchical attention model that encodes the structure of long documents through a diverse ensemble of attention heads. |
| Outcome: | The proposed model outperforms existing methods for detecting political bias in news articles. |
Self-Correcting Text-to-Video Generation with Misalignment Detection and Localized Refinement (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent text-to-video models struggle to faith-fully follow text prompts, authors say . authors propose a new refinement framework that detects fine-grained misalignments . |
| Approach: | They propose a video refinement framework that detects fine-grained misalignments . they propose preserving regions that should be preserved rather than regenerated . |
| Outcome: | The proposed framework detects fine-grained misalignments and performs targeted corrections . it preserves correctly generated entities, segments regions across frames, and regenerates problematic regions . |
Video-Skill-CoT: Skill-based Chain-of-Thoughts for Domain-Adaptive Video Reasoning (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for chain-of-thought reasoning fail to adapt to domain-specific skills over video content. |
| Approach: | They propose a framework that automatically constructs and leverages skill-aware CoT supervisions for domain-adaptive video reasoning. |
| Outcome: | The proposed framework outperforms strong baselines on three video understanding benchmarks. |
Fine-grained Image Captioning with CLIP Reward (2022.findings-naacl)
Copied to clipboard
| Challenge: | Modern image captioning models are usually trained with text similarity objectives . reference captions often describe only the most salient objects in images . |
| Approach: | They propose to use CLIP to calculate multi-modal similarity and use it as a reward function . they propose a simple finetuning strategy to improve grammar that does not require extra text annotation. |
| Outcome: | The proposed model generates more distinctive captions than the CIDEroptimized model on text-to-image retrieval and fineCapEval. |
RotBench: Evaluating Multi-modal Large Language Models on Identifying Image Rotation (2026.eacl-long)
Copied to clipboard
| Challenge: | Multimodal Large Language Models (MLLMs) can identify the orientation of input images rotated 0°, 90°, 180°, and 270°. |
| Approach: | They propose a manually-filtered benchmark to evaluate MLLMs' ability to accurately identify rotation in input images. |
| Outcome: | The proposed model improves on the 'rotational cues' of 360° and 180° images, but not 90° and 270° rotations. |