Papers by Jin-Ge Yao
Learning Latent Semantic Annotations for Grounding Natural Language to Structured Data (D18-1)
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| Challenge: | Existing work on grounded language learning does not capture the semantics of correspondences between structured world state representations and texts. |
| Approach: | They propose to learn explicit latent semantic annotations from paired structured tables and texts . they use an adapted semi-hidden Markov model to impose a soft constraint to further improve performance . |
| Outcome: | The proposed framework improves on a semi-hidden Markov model and extracts templates for language generation. |
Using Intermediate Representations to Solve Math Word Problems (P18-1)
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| Challenge: | Existing approaches to solving math word problems do not include higher-order operations that cannot be explicitly represented in equations. |
| Approach: | They propose an iterative labeling framework that generates intermediate forms and executes them to obtain the final answers. |
| Outcome: | The proposed model outperforms existing models in solving math word problems. |
FlagEvalMM: A Flexible Framework for Comprehensive Multimodal Model Evaluation (2025.acl-demo)
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| Challenge: | FlagEvalMM is an evaluation framework designed to assess multimodal models . it is designed to be used for vision-language understanding and generation tasks . |
| Approach: | They propose an evaluation framework that decouples model inference from evaluation through an independent evaluation service. |
| Outcome: | The evaluation framework offers accurate and efficient insights into model strengths and limitations. |
Operation-guided Neural Networks for High Fidelity Data-To-Text Generation (D18-1)
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| Challenge: | Recent neural models for data-to-text generation generate descriptions that are not consistent with structured data. |
| Approach: | They propose a framework for data-to-text generation that uses symbolic operations to generate texts from structured data. |
| Outcome: | The proposed framework improves the fidelity of the generated texts to the input structured data. |
Data2Text Studio: Automated Text Generation from Structured Data (D18-2)
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| Challenge: | Data2Text Studio is a platform for automated text generation from structured data. |
| Approach: | They conduct experiments on RotoWire datasets for template extraction and text generation . they find that the Semi-HMMs model improves interactivity and interpretability . |
| Outcome: | The proposed model improves on template extraction and text generation tasks on RotoWire datasets. |
FlagEval-Arena: A Side-by-Side Comparative Evaluation Platform for Large Language Models and Text-Driven AIGC (2025.acl-demo)
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Jing-Shu Zheng, Richeng Xuan, Bowen Qin, Zheqi He, Tongshuai.ren Tongshuai.ren, Xuejing Li, Jin-Ge Yao, Xi Yang
| Challenge: | a new evaluation platform for large language models and text-driven AIGCs is available for free. |
| Approach: | They propose an evaluation platform for side-by-side comparisons of large language models and text-driven AIGC systems. |
| Outcome: | a new evaluation platform for large language models and text-driven AIGC systems is available for free . the platform is more focused on the Chinese language and more models developed by Chinese institutes . |
Towards Improving Neural Named Entity Recognition with Gazetteers (P19-1)
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| Challenge: | Currently, neural models for named entity recognition are based on data-driven models, with a strong emphasis on getting rid of the efforts for collecting external resources or designing hand-crafted features. |
| Approach: | They propose to use external gazetteers to efficiently access annotated data to generalize beyond the annotation of entities. |
| Outcome: | The proposed model can access external gazetteers while avoiding the effort to design hand-crafted features. |
A Simple Recipe towards Reducing Hallucination in Neural Surface Realisation (P19-1)
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| Challenge: | Recent neural language generation systems often hallucinate contents when trained on loosely corresponding pairs of the input structure and text. |
| Approach: | They propose to integrate a language understanding module for data refinement with self-training iterations to induce strong equivalence between the input data and the paired text. |
| Outcome: | Experiments on the E2E challenge dataset show that the proposed framework reduces relative unaligned noise by 50% compared with the current state-of-the-art ensemble generator. |
Issues with Entailment-based Zero-shot Text Classification (2021.acl-short)
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| Challenge: | Pre-trained BERT models with no fine-tuning can yield competitive performance against BERT fine- tuned for NLI. |
| Approach: | They propose to use any target label into a sentence of hypothesis and verify whether it could be entailed by the input. |
| Outcome: | The proposed models perform better than models fine-tuned for BERT, but the results are in general negative. |