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Found 28 papers, 17 papers with code
Better Transition-Based AMR Parsing with a Refined Search Space
This paper introduces a simple yet effective transition-based system for Abstract Meaning Representation (AMR) parsing.
Attention Guided Graph Convolutional Networks for Relation Extraction
Dependency trees convey rich structural information that is proven useful for extracting relations among entities in text.
Ranked #25 on
Relation Extraction
on TACRED
Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning
We focus on graph-to-sequence learning, which can be framed as transducing graph structures to sequences for text generation.
Reasoning with Latent Structure Refinement for Document-Level Relation Extraction
Document-level relation extraction requires integrating information within and across multiple sentences of a document and capturing complex interactions between inter-sentence entities.
Ranked #9 on
Relation Extraction
on GDA
Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation
With the help of these strategies, we are able to train a model with fewer parameters while maintaining the model capacity.
FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information
Fact verification has attracted a lot of attention in the machine learning and natural language processing communities, as it is one of the key methods for detecting misinformation.
A Survey on Automated Fact-Checking
Fact-checking has become increasingly important due to the speed with which both information and misinformation can spread in the modern media ecosystem.
Uncovering Main Causalities for Long-tailed Information Extraction
Information Extraction (IE) aims to extract structural information from unstructured texts.
CHEF: A Pilot Chinese Dataset for Evidence-Based Fact-Checking
The explosion of misinformation spreading in the media ecosystem urges for automated fact-checking.
Scene Graph Modification as Incremental Structure Expanding
A scene graph is a semantic representation that expresses the objects, attributes, and relationships between objects in a scene.
METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets
To further investigate tweet users' attitudes toward specific entities, 4 types of entities (Person, Organization, Drug, and Vaccine) are selected and annotated with user sentiments, resulting in a targeted sentiment dataset with 9, 101 entities (in 5, 278 tweets).
GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge Features
Our framework is effective, robust and flexible, and is a plug-and-play module that can be combined with different backbones and Graph Neural Networks (GNNs) to generate a task-specific graph representation from various graph and non-graph data.
Read it Twice: Towards Faithfully Interpretable Fact Verification by Revisiting Evidence
In light of this, we propose a fact verification model named ReRead to retrieve evidence and verify claim that: (1) Train the evidence retriever to obtain interpretable evidence (i. e., faithfulness and plausibility criteria); (2) Train the claim verifier to revisit the evidence retrieved by the optimized evidence retriever to improve the accuracy.
Multimodal Automated Fact-Checking: A Survey
In this survey, we conceptualise a framework for AFC including subtasks unique to multimodal misinformation.
AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web
Existing datasets for automated fact-checking have substantial limitations, such as relying on artificial claims, lacking annotations for evidence and intermediate reasoning, or including evidence published after the claim.
Give Me More Details: Improving Fact-Checking with Latent Retrieval
Evidence plays a crucial role in automated fact-checking.
Multimodal Relation Extraction with Cross-Modal Retrieval and Synthesis
Multimodal relation extraction (MRE) is the task of identifying the semantic relationships between two entities based on the context of the sentence image pair.
DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning
Dual Queries first query LLM to obtain LLM-generated knowledge such as CoT, then query the retriever to obtain the final exemplars via both question and the knowledge.
Do Large Language Models Know about Facts?
Large language models (LLMs) have recently driven striking performance improvements across a range of natural language processing tasks.
TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models
Automated theorem proving (ATP) has become an appealing domain for exploring the reasoning ability of the recent successful generative language models.
Are LLMs Rigorous Logical Reasoner? Empowering Natural Language Proof Generation with Contrastive Stepwise Decoding
Logical reasoning remains a pivotal component within the realm of artificial intelligence.
PROXYQA: An Alternative Framework for Evaluating Long-Form Text Generation with Large Language Models
LLMs are prompted to generate extensive content in response to these meta-questions.
Do We Need Language-Specific Fact-Checking Models? The Case of Chinese
This paper investigates the potential benefits of language-specific fact-checking models, focusing on the case of Chinese.
YODA: Teacher-Student Progressive Learning for Language Models
With the teacher's guidance, the student learns to iteratively refine its answer with feedback, and forms a robust and comprehensive understanding of the posed questions.
Evaluating Robustness of Generative Search Engine on Adversarial Factual Questions
To this end, we propose evaluating the robustness of generative search engines in the realistic and high-risk setting, where adversaries have only black-box system access and seek to deceive the model into returning incorrect responses.
Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators
Large Language Models (LLMs) have demonstrated promising capabilities as automatic evaluators in assessing the quality of generated natural language.
Learning From Correctness Without Prompting Makes LLM Efficient Reasoner
Large language models (LLMs) have demonstrated outstanding performance across various tasks, yet they still exhibit limitations such as hallucination, unfaithful reasoning, and toxic content.
The Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS) Shared Task
The Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS) shared task, asks participating systems to determine whether human-authored claims are Supported or Refuted based on evidence retrieved from Wikipedia (or NotEnoughInfo if the claim cannot be verified).
