Papers by Jiaze Li

8 papers
On Tree-Based Neural Sentence Modeling (D18-1)

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Challenge: Existing tree-based sentence modeling approaches adopt syntactic parsing trees as the explicit structure prior.
Approach: They replace parsing trees with trivial trees to study their effectiveness . they found that tree-based sentence modeling gives better results when crucial words are closer to the final representation .
Outcome: The proposed tree-based sentences have shown better results on many downstream tasks.
iMOVE : Instance-Motion-Aware Video Understanding (2025.findings-acl)

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Challenge: Recent advances in Video Large Language Models have led to rapid development, significantly enhancing the capture of overall video semantics and achieving remarkable performance in general video understanding tasks.
Approach: They propose a large-scale instance-motion-aware video instruction-tuning dataset iMOVE that utilizes Event-awful Spatiotemporal Efficient Modeling to retain informative instance spatiotemporal motion details while maintaining computational efficiency.
Outcome: The proposed model excels in video temporal understanding and general video understanding.
Xiaomingbot: A Multilingual Robot News Reporter (2020.acl-demos)

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Challenge: Xiaomingbot is a multilingual and multimodal software robot with four capabilities: news generation, news translation, news reading and avatar animation.
Approach: They propose to build a multilingual and multimodal software robot with four inte- gal capabilities: news generation, news translation, news reading and avatar animation.
Outcome: The proposed system generates Chinese news, then reads it in multiple languages and generates an animated avatar reading it.
MTG: A Benchmark Suite for Multilingual Text Generation (2022.findings-naacl)

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Challenge: Using MTG, we train and evaluate multilingual text generation models using human-annotated data.
Approach: They propose a multilingual multiway text generation dataset with 400k human-annotated data that includes four generation tasks across five languages.
Outcome: The proposed dataset includes four generation tasks across five languages (English, German, French, Spanish and Chinese) it provides comprehensive evaluations with diverse generation scenarios.
Contrastive Aligned Joint Learning for Multilingual Summarization (2021.findings-acl)

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Challenge: Existing summarization systems for multilingual text summarizing are limited due to the lack of large-scale data in multiple languages.
Approach: They propose a multilingual summarization system that can understand documents in multiple languages and generate summaries in the corresponding language.
Outcome: The proposed model improves over monolingual models in all languages and transferable to other languages.
Multilingual Generation in Abstractive Summarization: A Comparative Study (2024.lrec-main)

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Challenge: Existing models for multilingual generation lack thorough analysis due to extensive linguistic diversity.
Approach: They propose to classify multilingual generation methodologies into three categories based on their underlying modeling principles . they introduce an automatic metric to mitigate spurious correlations associated with language mixing .
Outcome: The proposed model improves in high-resource, low-resourced, and zero-shot scenarios.
Probabilistic Graph Reasoning for Natural Proof Generation (2021.findings-acl)

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Challenge: Existing approaches to reasoning over formal representations do not explicitly consider inter-dependency between answers and proofs.
Approach: They propose a novel approach for joint answer prediction and proof generation using an induced graphical model.
Outcome: The proposed approach achieves 10%-30% improvement on QA accuracy in evaluations under diverse conditions.
Gradient-Based Adversarial Factual Consistency Evaluation for Abstractive Summarization (2021.emnlp-main)

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Challenge: Abstractive summarization models often produce inconsistent statements or false facts.
Approach: They propose an efficient weak-supervised adversarial data augmentation approach to generate factual consistency datasets by backpropagating gradients on token embeddings.
Outcome: The proposed model can make interpretable factual errors tracing on public datasets and is cost-effective.

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