A Spreading Activation Framework for Tracking Conceptual Complexity of Texts (P19-1)
Copied to clipboard
| Challenge: | Existing models for assessing conceptual complexity of texts are lacking . conceptual complexity accounts for background knowledge necessary to understand mentioned concepts . |
| Approach: | They propose an unsupervised approach for assessing conceptual complexity of texts based on spreading activation using DBpedia knowledge graph as a proxy to long-term memory. |
| Outcome: | The proposed model outperforms current state of the art in assessing conceptual complexity of texts. |
Similar Papers
Automatic Assessment of Conceptual Text Complexity Using Knowledge Graphs (C18-1)
Copied to clipboard
| Challenge: | Existing methods to assess text complexity only at lexical and syntactic levels have not been attempted. |
| Approach: | They propose to automatically estimate conceptual complexity using graph-based measures on a large knowledge base. |
| Outcome: | The proposed measures achieve high discriminative power even in a default setup. |
CoCo: A Tool for Automatically Assessing Conceptual Complexity of Texts (2020.lrec-1)
Copied to clipboard
| Challenge: | Traditional text complexity assessment only takes into account lexical and lexiconal complexity. |
| Approach: | They propose a tool for automatic assessment of conceptual text complexity based on the current state-of-the-art unsupervised approach . they compare the current implementation with the state of the art and discuss the influence of the choice of entity linker on the performance of the tool. |
| Outcome: | The proposed tool can be personalized and adapted to the needs of struggling readers. |
When Shallow is Good Enough: Automatic Assessment of Conceptual Text Complexity using Shallow Semantic Features (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing approaches to automatic assessment of text complexity focus on syntactic and lexical complexity. |
| Approach: | They propose to use graph-based deep semantic features to automatically assess conceptual text complexity by using DBpedia as a proxy to human knowledge. |
| Outcome: | The proposed features outperform the state-of-the-art features on pairwise comparison of two versions of the same text and five-level classification task. |
COCO-EX: A Tool for Linking Concepts from Texts to ConceptNet (2021.eacl-demos)
Copied to clipboard
| Challenge: | ConceptNet is a semantic network which contains general commonsense facts about the world, e.g., Birds can fly or Computers are used for sending e-mails. |
| Approach: | They propose a tool for Extracting Concepts from texts and linking them to ConceptNet, using the maximum relational information stored in ConceptNet. |
| Outcome: | The proposed method extracts meaningful concepts from natural language texts and links them to conjunct concept nodes in ConceptNet, utilizing the maximum of relational information stored in the KnowledgeGraph. |
Inspecting the concept knowledge graph encoded by modern language models (2021.findings-acl)
Copied to clipboard
| Challenge: | Pre-trained language models are used to solve tasks such as summarization and information retrieval. |
| Approach: | They propose to use word embeddings, text generators, context encoders to extract underlying knowledge graphs of nine influential language models. |
| Outcome: | The proposed model is able to encode word embeddings, text generators, and context encoders, but suffers from several inaccuracies. |
Synapse: Empowering LLM Agents with Episodic-Semantic Memory via Spreading Activation (2026.findings-acl)
Copied to clipboard
Hanqi Jiang, Junhao Chen, Yi Pan, Ling Chen, Weihang You, Yifan Zhou, Ruidong Zhang, Yohannes Abate, Tianming Liu
| Challenge: | Large Language Models excel at generalized reasoning, but lack the ability to accumulate experiences and maintain narrative coherence over long horizons. |
| Approach: | They propose a unified memory architecture that transcends static vector similarity. |
| Outcome: | The proposed model outperforms state-of-the-art methods in temporal and multihop reasoning tasks. |
Evaluating LLMs for Targeted Concept Simplification for Domain-Specific Texts (2024.emnlp-main)
Copied to clipboard
| Challenge: | Simplifying the entire text makes it understandable but sometimes removes important details. |
| Approach: | They propose a simplification task for rewriting text to help readers comprehend text containing unfamiliar concepts and introduce a dataset of 22k definitions from 13 academic domains paired with a difficult concept within each definition. |
| Outcome: | The proposed model outperforms open-source and commercial models on the task and human judges prefer explanations over simplifications of the difficult concept. |
Relational Memory-Augmented Language Models (2022.tacl-1)
Copied to clipboard
| Challenge: | Existing language models rely on word correlation and are difficult to interpret . existing models often lack explicit representations for such information . |
| Approach: | They propose a memory-augmented approach to condition autoregressive language models on knowledge graphs. |
| Outcome: | The proposed model improves perplexity and bits per character in an autoregressive language model . it is complementary to token-based memory and enables causal interventions . |
One Size Does Not Fit All: The Case for Personalised Word Complexity Models (2022.findings-naacl)
Copied to clipboard
| Challenge: | Complex word identification (CWI) aims to identify words in a text that are difficult for a reader to understand and therefore benefit from simplification. |
| Approach: | They propose to use a novel active learning framework to tailor models to individual readers and release a dataset of complexity annotations and models as a benchmark for further research. |
| Outcome: | The proposed model can be tailored to individual readers and released as a benchmark for future research. |
The Language of Trauma: Modeling Traumatic Event Descriptions Across Domains with Explainable AI (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Psychological trauma can manifest following various distressing events, but studies focus on a single aspect of trauma, often neglecting the transferability of findings across different scenarios. |
| Approach: | They propose a language model that fine-tunes a single aspect of trauma to better predict traumatic events across domains. |
| Outcome: | The proposed model outperforms large language models on trauma-related datasets . it also outperformed models on court data, counseling conversations, and forum posts . |