Papers by Yutong Shao
Evaluating Machine Translation Performance on Chinese Idioms with a Blacklist Method (L18-1)
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| Challenge: | idiom translation is a challenging problem in machine translation because meaning is non-compositional and literal translations are likely to be wrong. |
| Approach: | They propose a method to evaluate the quality of idiom translation of MT systems by a blacklist of literal translations. |
| Outcome: | The proposed method detects that a sizable number of idioms are mistranslated (46.1%) and that literal translation error is a common error type. |
Generative Interfaces for Language Models (2026.findings-acl)
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| Challenge: | Large language models are increasingly seen as assistants, copilots, and consultants . however, their linear request-response format often makes interactions inefficient in multi-turn tasks . |
| Approach: | They propose a paradigm in which large language models respond to user queries by generating user interfaces that enable more adaptive and interactive engagement. |
| Outcome: | The proposed paradigm outperforms traditional chat-based interfaces in many tasks and interaction patterns. |
Spotlighter: Revisiting Prompt Tuning from a Representative Mining View (2025.findings-emnlp)
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| Challenge: | Spotlighter is a lightweight token-selection framework that enhances accuracy and efficiency in prompt tuning. |
| Approach: | They propose a token-selection framework that enhances accuracy and efficiency in prompt tuning by preserving only the top-scoring tokens for downstream prediction. |
| Outcome: | The proposed framework outperforms CLIP by up to 11.19% in harmonic mean accuracy and achieves 0.8K additional FPS, with only 21 extra parameters. |
Low-resource Entity Set Expansion: A Comprehensive Study on User-generated Text (2022.findings-naacl)
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| Challenge: | Existing benchmarks for entity set expansion (ESE) are limited to well-formed text and well-defined concepts. |
| Approach: | They propose to use user-generated text to assess the generalizability of ESE methods by identifying phenomena such as non-named entities, multifaceted entities and vague concepts. |
| Outcome: | The proposed methods are based on user-generated text to assess their generalizability and performance. |
On Linearizing Structured Data in Encoder-Decoder Language Models: Insights from Text-to-SQL (2024.naacl-long)
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| Challenge: | Structured data is prevalent in tables, databases, and knowledge graphs, but there is a gap in our understanding of how these linearization-based methods handle structured data, which is inherently non-linear. |
| Approach: | They investigate the linear handling of structured data in encoder-decoder language models, specifically T5. |
| Outcome: | The proposed model can mimic human-designed processes such as schema linking and syntax prediction, and it can be compressed due to modality fusion redundancy. |
ChartDialogs: Plotting from Natural Language Instructions (2020.acl-main)
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| Challenge: | a new dataset of conversational plotting agents is developed to facilitate the development of such agents. |
| Approach: | They propose a dataset that contains over 15,000 dialog turns from matplotlib's most popular plotting library. |
| Outcome: | The proposed system achieves 61% plotting accuracy, compared to the previous method. |
Interactive Plot Manipulation using Natural Language (2021.naacl-demos)
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| Challenge: | a new interactive plotting agent is available for programming with natural language . the interactive aspect allows users to manipulate plots using natural language instructions. |
| Approach: | They propose an interactive natural language interface for plotting that maps language to plot updates. |
| Outcome: | The proposed system maps language to plot updates within an interactive programming environment. |