Numerically Grounded Language Models for Semantic Error Correction

Understand

Semantic error detection and correction is an important task for applications such as fact checking, speech-to-text or grammatical error correction.

• Current approaches generally focus on relatively shallow semantics and do not account for numeric quantities.
• Our approach uses language models grounded in numbers within the text.
• Such groundings are easily achieved for recurrent neural language model architectures, which can be further conditioned on incomplete background knowledge bases.