Paper: Minimally Supervised Model of Early Language Acquisition

ACL ID W09-1112
Title Minimally Supervised Model of Early Language Acquisition
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2009

Theories of human language acquisition as- sume that learning to understand sentences is a partially-supervised task (at best). Instead of using ‘gold-standard’ feedback, we train a simplified “Baby” Semantic Role Labeling system by combining world knowledge and simple grammatical constraints to form a po- tentially noisy training signal. This combina- tion of knowledge sources is vital for learn- ing; a training signal derived from a single component leads the learner astray. When this largely unsupervised training approach is ap- plied to a corpus of child directed speech, the BabySRL learns shallow structural cues that allow it to mimic striking behaviors found in experiments with children and begin to cor- rectly identify agents in a sentence.