Papers with ARI
Automatic Rule Induction for Efficient Semi-Supervised Learning (2022.findings-emnlp)
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| Challenge: | Existing approaches to generalize from labeled and unlabeled data are difficult to explain and behave unreliably. |
| Approach: | They propose a framework for automatic discovery and integration of symbolic rules into pretrained transformer models by using an attention mechanism. |
| Outcome: | The proposed framework can improve state-of-the-art methods with no manual effort and minimal computational overhead. |
Multi-party Multimodal Conversations Between Patients, Their Companions, and a Social Robot in a Hospital Memory Clinic (2024.eacl-demo)
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Angus Addlesee, Neeraj Cherakara, Nivan Nelson, Daniel Hernandez Garcia, Nancie Gunson, Weronika Sieińska, Christian Dondrup, Oliver Lemon
| Challenge: | a new spoken dialogue system is being developed for hospitals and hospitals to enable multi-party interactions . a social robot can be used to have multi-part conversations with patients and their companions . |
| Approach: | They describe a spoken dialogue system that allows patients to have multi-party conversations with their companions . they use speech and video input to generate both speech and gestures - arm, head, and eye movements . |
| Outcome: | The proposed system generates human-like clarification requests when the patient pauses mid-utterance, answers in-domain questions, and responds appropriately to out-of-domain requests. |
Event Ontology Completion with Hierarchical Structure Evolution Networks (2023.emnlp-main)
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| Challenge: | Existing methods for event detection require predefined schemas, but manual defining is expensive and labor-intensive. |
| Approach: | They propose a task to achieve event clustering, hierarchy expansion and type naming . they propose 'neighbor Contrastive Clustering' module and a Hierarchy-Aware Linking module . |
| Outcome: | The proposed method outperforms baseline methods on three datasets. |
FAIR: Filtering of Automatically Induced Rules (2024.eacl-long)
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| Challenge: | Existing methods to reduce the human annotation efforts require a diverse set of rules to assign labels to unlabeled data. |
| Approach: | They propose an automatic rule-filtering algorithm to filter out a large set of automatically created rules from a small set of labeled features. |
| Outcome: | The proposed approach achieves statistically significant results over existing methods. |
U-CORE: A Unified Deep Cluster-wise Contrastive Framework for Open Relation Extraction (2023.tacl-1)
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| Challenge: | Existing methods for Relation Extraction (RE) are limited due to the overlap between predefined and undefined relations. |
| Approach: | They propose a unified framework for both Zero-shot and Unsupervised Relation Extraction tasks by leveraging techniques from Contrastive Learning and Clustering. |
| Outcome: | The proposed framework improves on three well-known datasets showing an average improvement of 7.35% ARI on Zero-shot ORE tasks and 15.24% ARI for Unsupervised ORE. |
Temporal Knowledge Question Answering via Abstract Reasoning Induction (2024.acl-long)
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| Challenge: | a new method to enhance temporal knowledge reasoning in large language models addresses this challenge . Abstract Reasoning Induction (ARI) framework provides factual knowledge support to LLMs . |
| Approach: | They propose an abstract reasoning induction framework which divides temporal reasoning into two phases: Knowledge agnostic and Knowledge-based. |
| Outcome: | The proposed method achieves significant gains on two temporal QA datasets. |
PITA: Prompting Task Interaction for Argumentation Mining (2024.acl-long)
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| Challenge: | Argumentation mining (AM) aims to detect arguments and their inherent relations from textual compositions. |
| Approach: | They propose a method to model the inter-relationships among three subtasks within a generative framework. |
| Outcome: | The proposed method achieves state-of-the-art performance on two AM benchmarks. |
Time-aware ReAct Agent for Temporal Knowledge Graph Question Answering (2025.findings-naacl)
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| Challenge: | Existing solutions for temporal knowledge graph question answering lack sufficient temporal constraints in retrieval process. |
| Approach: | They propose a temporal knowledge graph question answering framework that integrates temporal constraints into information retrieval. |
| Outcome: | The proposed framework achieves a 41.3% improvement over the baseline model and a 32.2% gain compared to the Abstract Reasoning Induction (ARI) method. |
Leveraging External Knowledge for Historical Document Restoration via Retrieval-Augmented Large Language Models (2026.findings-acl)
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| Challenge: | Historical documents suffer from illegibility due to physical deterioration and damage due to deteriorating materials. |
| Approach: | a new framework leverages large language models with retrieval-augmented generation to restore historical documents. authors propose a framework that leverages implicit knowledge of pre-trained LLMs with explicitly retrieved external context. |
| Outcome: | a new framework outperforms existing methods for restoration of historical documents in Korean . the proposed model can restore both general characters and named entities, the authors say . |