Papers by Irina Saparina
SPARQLing Database Queries from Intermediate Question Decompositions (2021.emnlp-main)
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| Challenge: | Using annotated datasets is difficult as it requires query-language expertise. |
| Approach: | They propose a crowdsourcing pipeline to annotate natural language questions using intermediate question representations. |
| Outcome: | The proposed pipeline reduces the burden of annotating a large dataset with queries by using intermediate question representations. |
Searching for Better Database Queries in the Outputs of Semantic Parsers (2023.findings-eacl)
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| Challenge: | generating a database query from a question in natural language is a longstanding task . the task is amplified when the system needs to generalize to databases unseen at training. |
| Approach: | They propose to generalize a query to databases unseen at training . they use state-of-the-art semantic parsers to find queries that meet the criterion . |
| Outcome: | The proposed approach finds that many queries pass all tests on different datasets. |
Disambiguate First, Parse Later: Generating Interpretations for Ambiguity Resolution in Semantic Parsing (2025.findings-acl)
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| Challenge: | Natural language interfaces are often ambiguous, vague, or underspecified, giving rise to multiple valid interpretations. |
| Approach: | They propose a modular approach that resolves ambiguity using natural language interpretations before mapping them to logical forms. |
| Outcome: | The proposed approach improves interpretation coverage and generalizes across datasets with different annotation styles, database structures, and ambiguity types. |
Long-Context Reasoning Through Proxy-Based Chain-of-Thought Tuning (2026.acl-long)
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| Challenge: | Recent large language models support inputs of up to 10 million tokens, yet they perform poorly on long-context tasks. |
| Approach: | They propose a training framework that transfers reasoning capabilities from proxy contexts to full long contexts. |
| Outcome: | The proposed framework outperforms baseline models with reduced computational overhead. |
Improving Generalization in Semantic Parsing by Increasing Natural Language Variation (2024.eacl-long)
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| Challenge: | Existing approaches generate question reformulations via models trained on Spider or only introduce local changes. Existing methods generate question form reformulation but lack robustness. |
| Approach: | They use data augmentation to enhance the robustness of text-to-SQL parsers against natural language variations by generating more realistic and diverse questions. |
| Outcome: | The proposed model improves on the new spider dataset by using a few prompts. |