Papers by Shijia Liu

7 papers
Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions (2020.emnlp-main)

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Challenge: A grammatical gender system divides a lexicon into a small number of fixed categories with fixed usage across speakers.
Approach: They propose to define gender systems extensionally to reduce comparisons to cluster evaluation by comparing pairwise overlaps between gender systems.
Outcome: The proposed measures are based on a phylogenetic tree over extant Indo-European languages.
PlanGenLLMs: A Modern Survey of LLM Planning Capabilities (2025.acl-long)

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Challenge: Existing studies have focused on developing LLMs to automate complex planning tasks.
Approach: They propose to provide a comprehensive overview of current LLM planners to fill this gap . they examine performance criteria including completeness, executability, optimality, representation, generalization, and efficiency .
Outcome: The proposed survey examines performance criteria for LLM planners and highlights their strengths and weaknesses.
Detecting de minimis Code-Switching in Historical German Books (2020.coling-main)

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Challenge: Code-switching has drawn scholarly attention in computational linguistics and natural language processing from many different perspectives.
Approach: They propose to compare informal code-switching to its appearance in more formal registers by annotating and inspecting the German textarchives.
Outcome: The proposed classifiers can help reduce errors when speech recognition is applied to a large corpus with rare embedded languages.
On the Idiosyncrasies of the Mandarin Chinese Classifier System (N19-1)

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Challenge: idiosyncrasies of the Chinese classifier system have been studied, but little work has been done to quantify them with statistical methods.
Approach: They propose an information-theoretic approach to measuring idiosyncrasies in Mandarin Chinese by calculating the mutual information between the distribution over classifiers and distributions over other linguistic quantities.
Outcome: The proposed method reduces uncertainty in Mandarin Chinese classifiers by knowing semantic information about nouns that they modify.
Through the Lens of History: Methods for Analyzing Temporal Variation in Content and Framing of State-run Chinese Newspapers (2025.naacl-long)

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Challenge: State-run newspapers are believed to strategically select and frame news articles to align with the shifting political tides of the country.
Approach: They analyze more than 50 years of articles from the People's Daily and Reference News to quantify differences in content and framing over time.
Outcome: The proposed methods show that the changes in name mentions and sentiment in news articles are more significant in People’s Daily than in Reference News .
RCBSF: A Multi-Agent Framework for Automated Contract Revision via Stackelberg Game (2026.findings-acl)

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Challenge: despite the adoption of Large Language Models (LLMs), contract revision remains impeded because generic models treat strict legal constraints as mere suggestions.
Approach: They propose a risk-constrained bilevel Stackelberg framework that models high-stakes revision as a strategic interaction rather than an open-ended conversation.
Outcome: The proposed framework achieves state-of-the-art performance with an average RRR of 84.21% and enhanced token efficiency.
Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTS (2026.findings-acl)

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Challenge: Existing approaches to retrieval-augmented generation still face problems with low context utilization and frequent hallucinations.
Approach: They propose a framework that reformulates retrieval and generation as constrained optimization and path planning.
Outcome: The proposed framework significantly improves reasoning accuracy on complex queries while reducing hallucinations.

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