Information-Theoretic Probing for Linguistic Structure (2004.03061v2)

Abstract: The success of neural networks on a diverse set of NLP tasks has led researchers to question how much these networks actually ``know'' about natural language. Probes are a natural way of assessing this. When probing, a researcher chooses a linguistic task and trains a supervised model to predict annotations in that linguistic task from the network's learned representations. If the probe does well, the researcher may conclude that the representations encode knowledge related to the task. A commonly held belief is that using simpler models as probes is better; the logic is that simpler models will identify linguistic structure, but not learn the task itself. We propose an information-theoretic operationalization of probing as estimating mutual information that contradicts this received wisdom: one should always select the highest performing probe one can, even if it is more complex, since it will result in a tighter estimate, and thus reveal more of the linguistic information inherent in the representation. The experimental portion of our paper focuses on empirically estimating the mutual information between a linguistic property and BERT, comparing these estimates to several baselines. We evaluate on a set of ten typologically diverse languages often underrepresented in NLP research---plus English---totalling eleven languages.

• The paper proposes a formal mutual information framework to reliably assess the encoding of linguistic structure in neural representations.
• It advocates for complex probes over simpler models, achieving tighter bounds on information estimation and reducing error.
• Empirical validation on BERT across 11 languages demonstrates significant, though variable, encoding of syntactic features.

Information-Theoretic Probing for Linguistic Structure

The paper "Information-Theoretic Probing for Linguistic Structure" (2004.03061) investigates the extent to which neural network representations encode linguistic information. It critiques the common practice in AI research of using simpler models to probe neural networks for linguistic attributes, proposing that more complex models should be leveraged to provide better mutual information estimates between the representations and linguistic properties.

Mutual Information as a Measure

The authors formalize probing as estimating the mutual information between a representation-valued random variable and a linguistic property-valued random variable. This formalization is presented as a more rigorous way to understand how much linguistic information is encoded in the representations. By evaluating this mutual information, one can discern the degree to which neural network-generated embeddings contain linguistic knowledge.

Probing Methodology

The paper highlights the importance of selecting the best performing probe for estimating mutual information accurately. Contrary to the traditional inclination to employ simpler, potentially less informative probes, the paper argues for the use of complex models that maximize mutual information estimates. Theoretical analysis shows that these models yield a tighter bound on information estimates, minimizing the estimation error.

Experimental Validation

The paper conducts an empirical analysis on BERT embeddings across a set of eleven linguistically diverse languages, including Basque, Czech, and English. The experimental results demonstrate that in many cases, these embeddings encode significant syntactic information, assessed through tasks like part-of-speech labeling and dependency labeling. However, the gain in mutual information over simpler word embeddings, such as fastText, varies greatly among languages.

Implications and Future Directions

Theoretical implications call into question the validity of probing for linguistic properties without considering the information processing inequality. The paper reveals that embeddings like BERT contain the same amount of information about linguistic properties as the original sentence, challenging the efficacy of standard probing tasks.

Control functions are introduced as crucial for providing baselines to understand contextual linguistic readings in embeddings. However, the findings also suggest an overarching need to formally define "ease of extraction" to better direct future probing efforts. This calls for a shift from focusing purely on probe complexity to assessing how easily syntactic information is extractable from embeddings.

Conclusion

This work asserts that the practice of linguistic probing requires reconsideration where the understanding of syntactic information should be contextualized within the capabilities of more complex probing models. This research provides a new perspective into the efficacy of neural embeddings in capturing linguistic structure, setting the stage for future exploration of effective probing methods and applications in NLP.

The authors provide the implementation on GitHub, contributing a valuable tool for advancing research in evaluating neural network representations for linguistic insights.