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.

Information-Theoretic Probing for Linguistic Structure: An Expert Analysis

The paper "Information-Theoretic Probing for Linguistic Structure," authored by Tiago Pimentel et al., investigates the challenge inherent in assessing how much linguistic knowledge is encoded in the representations produced by neural networks, particularly those networks utilized in NLP tasks. The authors propose an information-theoretic framework that operationalizes probing as the estimation of mutual information (MI) between representation-valued random variables and linguistic property–valued random variables. By doing so, the paper seeks to offer a more formal, quantitative foundation for understanding the extent to which linguistic information is accessible in these learned representations.

Core Contributions

1. Theoretical Framework: The paper frames the problem of probing as estimating the mutual information between neural network representations and linguistic properties. This was a significant departure from conventional probing methodologies, which often rely on simpler probes to avoid the representation learning the tasks instead of revealing encoded linguistic structures.
2. Use of Complex Probes: The authors challenge the prevailing assumption that simpler models as probes are more suitable. Instead, they argue that utilizing more complex, higher-performance probes provides tighter estimates of mutual information, hence revealing more accurate linguistic content within the representations.
3. Implementation and Evaluation: Through a rigorous experimental setup, the authors estimate mutual information between BERT representations and linguistic properties for eleven languages, providing significant empirical backing to their theoretical proposition. The results show varying degrees of linguistic information encoded in BERT across languages, challenging some existing beliefs about its universality and adequacy for different linguistic tasks.
4. Type-Level Controls: By introducing type-level control functions, the authors measure the incremental informational gain achieved by contextual embeddings over non-contextual embeddings in syntax-related tasks. This methodological innovation offers a benchmark to evaluate the existing models more robustly.

Empirical Findings

• Results Variance Across Languages: The empirical results showcased that BERT contains, at most, moderate additional information beyond type-level baselines (such as fastText), particularly for part-of-speech tagging tasks. Only in a limited set of languages did BERT exhibit substantial informational advantage in syntactic labeling tasks.
• Limited Efficiency in Contextual Tasks: For dependency labeling tasks, which inherently rely more on context, BERT showed a slight advantage over baseline models. However, its performance did not far exceed these baselines, suggesting that significant syntactic information may not be as effectively captured by BERT representations as previously thought.

Implications and Future Directions

The theoretical and empirical insights presented by Pimentel et al. carry important implications for future research directions in NLP and AI:

• Refining Probing Methodologies: The shift towards using complex probes as suggested by this paper might lead to the development of more accurate and fine-grained approaches to evaluating neural network representations.
• Contextual Embedding Analysis: By highlighting the limitations in additional information provided by models like BERT, this work underscores the necessity to refine and develop contextual embeddings that effectively leverage sentential context in linguistically diverse scenarios.
• Cross-Linguistic NLP Evaluation: The need for a better understanding of multilingual models' capabilities across different languages is emphasized, prompting further research into how deeply these models encode typological divergences and linguistic nuances.
• Probing Ease of Extraction: The notion of ease of extraction versus quantity of information provides an avenue for more nuanced discussions, potentially leading to new metrics for neural network evaluation that align more with practical implementations in downstream tasks.

Overall, this paper presents a meticulous and theoretically grounded approach to understanding linguistic structure encoding in neural network representations, prompting a re-evaluation of traditional probing methodologies and encouraging further inquiry into the field of information-theoretic analysis in NLP.