Papers with semantic components
Explicit Semantic Decomposition for Definition Generation (2020.acl-main)
Copied to clipboard
| Challenge: | Existing definition generation methods rely on decoding to extract semantic components of words. |
| Approach: | They propose a method which explicitly decomposes meaning of words into semantic components and models them with discrete latent variables for definition generation. |
| Outcome: | The proposed method outperforms existing methods on WordNet and Oxford benchmarks. |
Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies show that multi-level matching is difficult due to the scarcity of available annotated utterances. |
| Approach: | They propose a method where semantic components are distilled from utterances via multi-head self-attention with additional dynamic regularization constraints. |
| Outcome: | The proposed method improves representations of labeled and unlabeled instances while retaining high-level information. |
Normal-Abnormal Decoupling Memory for Medical Report Generation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for capturing nuanced visual information are prone to data bias and noise. |
| Approach: | They propose a normal-abnormal semantic decoupling network that utilizes abnormal pattern memory to optimize visual extraction through the extraction of abnormal semantics from the reports. |
| Outcome: | The proposed approach surpasses the current state-of-the-art methods on the benchmark MIMIC-CXR and shows excellent performance on the same dataset. |
Untangling Hate Speech Definitions: A Semantic Componential Analysis Across Cultures and Domains (2025.findings-naacl)
Copied to clipboard
| Challenge: | a new framework for analyzing hate speech definitions is proposed to address cultural differences in interpretations . a dataset of 493 definitions from more than 100 cultures is used to analyze hate speech . |
| Approach: | They propose a framework for a cross-cultural and cross-domain analysis of hate speech definitions . they use open-source LLMs to analyze the impact of different definitions on hate speech detection . |
| Outcome: | The proposed framework enables cross-cultural and cross-domain analysis of hate speech definitions . it reveals that many domains borrow definitions from one another without taking into account target culture . |
Knowledge Base Question Answering via Encoding of Complex Query Graphs (D18-1)
Copied to clipboard
| Challenge: | Existing KBQA methods focus on simpler questions and do not work well on complex questions . a knowledge-based question answering approach is able to answer complex questions using a standard knowledge base . |
| Approach: | They propose to encode query structure into a uniform vector representation of a question and its semantic components into . |
| Outcome: | The proposed approach outperforms existing methods on complex questions while staying competitive on simple questions. |
Label-Specific Dual Graph Neural Network for Multi-Label Text Classification (2021.acl-long)
Copied to clipboard
| Challenge: | Existing studies for multi-label text classification do not explore label-specific semantic components from documents. |
| Approach: | They propose a label-specific dual graph neural network that incorporates category information to learn label-related components from documents. |
| Outcome: | The proposed model outperforms state-of-the-art models on three benchmark datasets and achieves better performance with respect to tail labels. |
Where the Cat Sat: A Multilingual Framework for Spatial Language Understanding (2026.acl-long)
Copied to clipboard
| Challenge: | Existing work exhibits biases toward English and prepositional marking . Existing models are limited in understanding spatial relations across typologically diverse languages . |
| Approach: | They propose a multilingual framework and benchmark for spatial language understanding . they decompose spatial relations into surface elements and semantic components . their results suggest surface parsing does not entail spatial understanding - they argue . |
| Outcome: | The proposed framework and benchmark decomposes spatial relations into surface elements and semantic components. |