Papers by Ashley Lewis
Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction (2022.findings-acl)
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| Challenge: | Existing studies on semantic parsing focus on mapping a natural-language utterance to a logical form (LF) but natural language may contain ambiguity and variability, making this challenge difficult. |
| Approach: | They propose an interactive semantic parsing framework that explains the predicted LF step by step in natural language and enables the user to make corrections through natural-language feedback for individual steps. |
| Outcome: | The proposed framework improves parsing accuracy and transparency in a crowdsourced dialogue dataset. |
VISTA: Verification In Sequential Turn-based Assessment (2026.acl-long)
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| Challenge: | Existing metrics evaluate isolated responses or treat unverifiable content as errors, limiting their use for multi-turn dialogue. |
| Approach: | They propose a framework for evaluating conversational factuality via claim-level verification and sequential consistency tracking. |
| Outcome: | The proposed framework improves hallucination detection over existing benchmarks and models. |