Edward Choi, James Thorne, Yeonsu Kwon, Yohan Jo, Jiho Kim, Sungjin Park
We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| jiho283/FactKG | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| set_optim | Ran | jiho283/FactKG/claim_only/bert_classification.py pointer only (licence: NONE) · get_code("676757b292994728") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In real world applications, knowledge graphs (KG) are widely used in various domains (e.g. medical applications and dialogue agents). However, for fact verification, KGs have not been adequately utilized as a knowledge source. KGs can be a valuable knowledge source in fact verification due to their reliability and broad applicability. A KG consists of nodes and edges which makes it clear how concepts are linked together, allowing machines to reason over chains of topics. However, there are many challenges in understanding how these machine-readable concepts map to information in text. To enable the community to better use KGs, we introduce a new dataset, FACTKG: Fact Verification via Reasoning on Knowledge Graphs. It consists of 108k natural language claims with five types of reasoning: One-hop, Conjunction, Existence, Multi-hop, and Negation. Furthermore, FACTKG contains various linguistic patterns, including colloquial style claims as well as written style claims to increase practicality. Lastly, we develop a baseline approach and analyze FACTKG over these reasoning types. We believe FACTKG can advance both reliability and practicality in KG-based fact verification. 1
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2305.06590")
get_code_for_paper("2305.06590")
have("2305.06590")
Connect an agent — have() is free.