Zhijiang Guo

AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web

2023 · arXiv (Cornell University)

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. In this paper we introduce AVeriTeC, a …

Beyond Pass@1: Self-Play with Variational Problem Synthesis Sustains RLVR

2025 · arXiv (Cornell University)

Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as a key paradigm for post-training Large Language Models (LLMs), particularly for complex reasoning tasks. However, vanilla RLVR training has been shown to improve Pass@1 performance …

Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning

2019 · Transactions of the Association for Computational Linguistics

We focus on graph-to-sequence learning, which can be framed as transducing graph structures to sequences for text generation. To capture structural information associated with graphs, we investigate the problem of encoding graphs using graph convolutional …

Attention Guided Graph Convolutional Networks for Relation Extraction

2019

Dependency trees convey rich structural information that is proven useful for extracting relations among entities in text. However, how to effectively make use of relevant information while ignoring irrelevant information from the dependency trees remains …

Reasoning with Latent Structure Refinement for Document-Level Relation Extraction

2020

Document-level relation extraction requires integrating information within and across multiple sentences of a document and capturing complex interactions between inter-sentence entities. However, effective aggregation of relevant information in the document remains a challenging research question. …