Papers with inference module
Towards Better Document-level Relation Extraction via Iterative Inference (2022.emnlp-main)
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| Challenge: | Existing methods only consider feature information of entity pairs, but our model exploits both feature information and previous predictions of entity pair. |
| Approach: | They propose a document-level relation extraction model with iterative inference to extract relations between entities from raw texts. |
| Outcome: | The proposed model outperforms existing methods on three commonly-used datasets. |
Enhancing Extractive Question Answering in Multiparty Dialogues with Logical Inference Memory Network (2025.coling-main)
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| Challenge: | Existing models for multiparty dialogue question answering (QA) do not consider logical inference relations in multiparty dialogs, leading to suboptimal performance. |
| Approach: | They propose a memory network with logical inference for extractive QA in multiparty dialogues. |
| Outcome: | The proposed model achieves state-of-the-art on Molweni and FriendsQA benchmarks. |
RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning Based on Emotional Information (2025.acl-long)
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| Challenge: | Current methods for cross-domain misinformation detection focus on in-domain tasks and do not incorporate significant sentiment and emotion features. |
| Approach: | They propose a retrieval augmented (RAG) LLM framework that incorporates affective information into retrieval databases. |
| Outcome: | The proposed framework improves on three misinformation benchmarks. |
Making Reasoning Matter: Measuring and Improving Faithfulness of Chain-of-Thought Reasoning (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) are shown to perform better when asked to reason step-by-step before generating a final answer. |
| Approach: | They propose a framework to tailor small-sized LMs to generate correct reasoning steps and robustly reason over these steps. |
| Outcome: | The proposed framework outperforms four competitive baselines and improves the robustness and generalization ability of the reasoning LM, yielding higher performance on out-of-distribution test sets. |