Papers with natural language texts
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. |
Event Detection with Neural Networks: A Rigorous Empirical Evaluation (D18-1)
Copied to clipboard
| Challenge: | Neural network models have been the most successful for event detection, but they ignore syntactic relationships in the text. |
| Approach: | They propose a GRU-based model that combines syntactic information along with temporal structure through an attention mechanism. |
| Outcome: | The proposed model is competitive with existing models on a ACE2005 dataset. |
A UIMA Database Interface for Managing NLP-related Text Annotations (L18-1)
Copied to clipboard
| Challenge: | despite the use of UIMA as a document-based schema, it does not provide native database support. |
| Approach: | They develop a database interface to allow generic use of UIMA documents in database systems. |
| Outcome: | The framework is evaluated in relation to file system-based storage and provides data protection. |
HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to extract information graphs are difficult to scale to datasets with longer input texts because of their secondorder space/time complexities. |
| Approach: | They propose a Hybrid SPan GenerAtor that invertibly maps the information graph to an alternating sequence of nodes and edge types and generates them via a hybrid span decoder. |
| Outcome: | The proposed method outperforms state-of-the-art methods on the ACE05 dataset. |
Enhancing Structured Evidence Extraction for Fact Verification (2023.emnlp-main)
Copied to clipboard
| Challenge: | Open-domain fact verification requires extracting and integrating both structured and unstructured evidence to verify a claim. |
| Approach: | They propose a method to enhance the extraction of structured evidence by leveraging the row and column semantics of tables. |
| Outcome: | The proposed method achieves evidence recall of 60.01% on the test set, higher than the previous state-of-the-art method. |
Can Transformers Reason in Fragments of Natural Language? (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent work on natural language inference has identified two strands of research . |
| Approach: | They investigate whether neural networks have acquired logical principles from natural language . they use transformer-based models to detect valid inferences in controlled fragments of natural language. |
| Outcome: | The proposed model overfits to superficial patterns in the data rather than acquiring the logical principles governing reasoning in natural language fragments. |
Explicit Syntactic Guidance for Neural Text Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing text generation models follow the sequence-to-sequence paradigm . generative grammar suggests humans generate language by learning language grammar . |
| Approach: | They propose a syntax-guided generation schema that searches the syntax tree in a top-down direction. |
| Outcome: | The proposed method outperforms autoregressive baselines on paraphrase generation and machine translation. |
Document-level Causal Relation Extraction with Knowledge-guided Binary Question Answering (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing research on Event-Event Causal Relation Extraction (ECRE) has highlighted the lack of document-level modeling and causal hallucinations. |
| Approach: | They propose a Knowledge-guided binary Question Answering method with event structures for ECRE that utilizes cross-task knowledge in IE. |
| Outcome: | The proposed method achieves state-of-the-art on the MECI and MAVEN-ERE datasets. |