Uncovering Constraint-Based Behavior in Neural Models via Targeted Fine-Tuning

• URL: http://arxiv.org/abs/2106.01207v1
• Date: Wed, 2 Jun 2021 14:52:11 GMT
• Title: Uncovering Constraint-Based Behavior in Neural Models via Targeted Fine-Tuning
• Authors: Forrest Davis and Marten van Schijndel
• Abstract summary: We show that competing linguistic processes within a language obscure underlying linguistic knowledge. While human behavior has been found to be similar across languages, we find cross-linguistic variation in model behavior. Our results suggest that models need to learn both the linguistic constraints in a language and their relative ranking, with mismatches in either producing non-human-like behavior.
• Score: 9.391375268580806
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A growing body of literature has focused on detailing the linguistic knowledge embedded in large, pretrained language models. Existing work has shown that non-linguistic biases in models can drive model behavior away from linguistic generalizations. We hypothesized that competing linguistic processes within a language, rather than just non-linguistic model biases, could obscure underlying linguistic knowledge. We tested this claim by exploring a single phenomenon in four languages: English, Chinese, Spanish, and Italian. While human behavior has been found to be similar across languages, we find cross-linguistic variation in model behavior. We show that competing processes in a language act as constraints on model behavior and demonstrate that targeted fine-tuning can re-weight the learned constraints, uncovering otherwise dormant linguistic knowledge in models. Our results suggest that models need to learn both the linguistic constraints in a language and their relative ranking, with mismatches in either producing non-human-like behavior.

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