Papers with inference problem
Learning as Abduction: Trainable Natural Logic Theorem Prover for Natural Language Inference (2020.starsem-1)
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| Challenge: | a logic-based approach to Natural Language Inference is becoming less and less common . a new method uses semantic relations to abduct sentences from data . |
| Approach: | They propose a method to reverse a theorem-proving procedure to abduct semantic relations from data. |
| Outcome: | The proposed method improves the performance of the theorem prover on the SICK dataset by 1.4% while maintaining high precision (>94%) |
Fake News Detection using Deep Markov Random Fields (N19-1)
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| Challenge: | Existing deep-learning-based methods ignore the correlations among news articles and only consider each article individually. |
| Approach: | They propose a graph-theoretic method that inherits the power of deep learning while utilizing the correlations among the articles. |
| Outcome: | The proposed model improves on state-of-the-art models on well-known datasets. |
Variational Knowledge Graph Reasoning (N18-1)
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| Challenge: | Existing knowledge graphs have large amount of missing links, which limits their application . a recent study has proposed to design an automated inference model to complete the missing links in large knowledge graph. |
| Approach: | They propose to use variation inference to solve missing links in knowledge graphs . they use a posterior approximator, prior (path finder) and likelihood (path reasoner) |
| Outcome: | The proposed model achieves state-of-the-art on multiple datasets and is highly accurate. |
Inferential Machine Comprehension: Answering Questions by Recursively Deducing the Evidence Chain from Text (P19-1)
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| Challenge: | Experimental results on 3 popular datasets demonstrate the effectiveness of our approach. |
| Approach: | They propose a network to solve the inference problem by decomposing text into a series of attention-based reasoning steps. |
| Outcome: | The proposed network can be used to understand the meanings of given text to answer questions. |
MAPRO: Recasting Multi-Agent Prompt Optimization as Maximum a Posteriori Inference (2026.findings-eacl)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities across diverse tasks. |
| Approach: | They propose a framework that optimizes MAS prompts as a maximum a posteriori problem and then iteratively updates agent prompts. |
| Outcome: | The proposed framework surpasses manual and automated benchmarks in multiple tasks and provides general guidelines for building more reliable and principled multi-agent systems in the future. |
Question Answering by Reasoning Across Documents with Graph Convolutional Networks (N19-1)
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| Challenge: | Recent research in reading comprehension has focused on answering questions based on individual documents or even single paragraphs. |
| Approach: | They propose a neural model which integrates and reasons relying on information spread within documents and across multiple documents. |
| Outcome: | The proposed model achieves state-of-the-art on a multi-document question answering dataset, WikiHop. |