What if This Modified That? Syntactic Interventions via Counterfactual Embeddings

• URL: http://arxiv.org/abs/2105.14002v1
• Date: Fri, 28 May 2021 17:27:04 GMT
• Title: What if This Modified That? Syntactic Interventions via Counterfactual Embeddings
• Authors: Mycal Tucker, Peng Qian, and Roger Levy
• Abstract summary: Prior art aims to uncover meaningful properties within model representations via probes, but it is unclear how faithfully such probes portray information that the models actually use. We propose a technique, inspired by causal analysis, for generating counterfactual embeddings within models. In experiments testing our technique, we produce evidence that some BERT-based models use a tree-distance-like representation of syntax in downstream prediction tasks.
• Score: 19.3614797257652
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural language models exhibit impressive performance on a variety of tasks, but their internal reasoning may be difficult to understand. Prior art aims to uncover meaningful properties within model representations via probes, but it is unclear how faithfully such probes portray information that the models actually use. To overcome such limitations, we propose a technique, inspired by causal analysis, for generating counterfactual embeddings within models. In experiments testing our technique, we produce evidence that suggests some BERT-based models use a tree-distance-like representation of syntax in downstream prediction tasks.

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