Papers with logical operations
Logic-level Evidence Retrieval and Graph-based Verification Network for Table-based Fact Verification (2021.emnlp-main)
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| Challenge: | Existing methods leverage programs that contain rich logical information to enhance the verification process. |
| Approach: | They propose a table-based fact verification task as an evidence retrieval framework . they retrieve logic-level program-like evidence from the given table and a statement as supplementary evidence for the table . |
| Outcome: | The proposed method is able to retrieve logic-level program-like evidence from a table and a statement as supplementary evidence for the table. |
Open Political Corpora: Structuring, Searching, and Analyzing Political Text Collections with PoliCorp (2025.emnlp-demos)
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| Challenge: | PoliCorp provides researchers with access to rich textual data, enabling in-depth analysis of parliamentary discourse over time. |
| Approach: | They present a web portal that allows researchers to search political text corpora . the platform currently contains a collection of transcripts from the german parliament . |
| Outcome: | The proposed platform provides researchers with access to rich textual data, enabling in-depth analysis of parliamentary discourse over time. |
Beta-LR: Interpretable Logical Reasoning based on Beta Distribution (2024.findings-naacl)
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| Challenge: | Existing methods to capture logical information from text are limited by the uncertainty of the text. |
| Approach: | They propose a probabilistic embedding method to capture logical information from text . they embed texts into beta distributions on each dimension to eliminate logical uncertainty . |
| Outcome: | The proposed method achieves competitive performances on two datasets. |
Neural-based Mixture Probabilistic Query Embedding for Answering FOL queries on Knowledge Graphs (2022.emnlp-main)
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| Challenge: | Existing methods to embed entities and first-order logical queries in a vector space are often violated in real applications and limit their performance. |
| Approach: | They propose a Neural-based Mixture Probabilistic Query Embedding Model that embeds entities and first-order logical queries in a vector space. |
| Outcome: | The proposed model outperforms state-of-the-art methods on benchmark datasets. |
RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark (2020.emnlp-main)
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Tatiana Shavrina, Alena Fenogenova, Emelyanov Anton, Denis Shevelev, Ekaterina Artemova, Valentin Malykh, Vladislav Mikhailov, Maria Tikhonova, Andrey Chertok, Andrey Evlampiev
| Challenge: | Modern scientific methodology is beginning to explore universal transformers as an independent object of study. |
| Approach: | They propose a Russian general language understanding evaluation benchmark - Russian SuperGLUE . they provide a benchmark of nine tasks, human level evaluation and a leaderboard for the Russian language . |
| Outcome: | The proposed benchmark provides nine tasks for the Russian language and human level evaluation and leaderboard of transformer models. |
HMEAE: Hierarchical Modular Event Argument Extraction (D19-1)
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| Challenge: | Existing event extraction methods classify each argument role independently, ignoring conceptual correlations between different argument roles. |
| Approach: | They propose a Hierarchical Modular Event Argument Extraction model to provide inductive bias from the concept hierarchy of event argument roles. |
| Outcome: | The proposed model outperforms existing methods on real-world datasets and shows that it leverages useful knowledge from the concept hierarchy. |
Interactive Semantic Parsing with Reinforcement Learning for Knowledge Graph Reasoning (2026.findings-acl)
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| Challenge: | Existing approaches to improve LLM reliability rely on factual hallucinations . Existing methods rely only on graph traversal, resulting in imprecise retrieval and heavy post-processing burdens. |
| Approach: | They propose a framework that integrates knowledge Graphs as structured, high-fidelity buffers to enhance LLM reliability. |
| Outcome: | The proposed framework allows logical constraints to be dynamically interleaved with graph search while optimizing via reinforcement learning with only final answer feedback eliminates the need for gold program annotations. |