Probing Tasks Under Pressure
Authors: Alessio Miaschi, Chiara Alzetta, Dominique Brunato, Felice Dell'Orletta, Giulia Venturi (CNR-ILC, Università di Pisa, 2021)
Topic: Language Models

Abstract
Probing tasks are frequently used to evaluate whether the representations of Neural Language Models (NLMs) encode linguistic information. However, it is still questioned if probing classification tasks really enable such investigation or they simply hint for surface patterns in the data. We present a method to investigate this question by comparing the accuracies of a set of probing tasks on gold and automatically generated control datasets. Our results suggest that probing tasks can be used as reliable diagnostic methods to investigate the linguistic information encoded in NLMs representations.

Introduction
In recent years we saw the raise of a consistent body of work dealing with the use of probing tasks to test the linguistic competence learned by Neural Language Models (NLMs) <cit.>. The idea behind the probing paradigm is actually quite simple: using a diagnostic classifier, the probing model or probe, that takes the output representations of a NLM as input to perform a probing task, e.g. predict a given language property. If the probing model will predict the property correctly, then we can assume that the representations somehow encode that property. Studies relying on this method reported that NLMs representations do encode several properties related to morphological, syntactic and semantic information.Despite the amount of work, there are still several open questions concerning their use <cit.>: which probing model should we use for assessing the linguistic competence of a NLM? Are probes the most effective strategy to achieve such goal? These questions fostered two complementary lines of research. The first one is devoted to modifying the architecture of the current probing models; the other one is focused on evaluating the effectiveness of probing models. Both are still not well investigated issues, although their importance for advancing the research on the evaluation of NLMs linguistic competences has been widely recognized. Among the first line of research, dealing with the design of probing classifiers, several works investigate which model should be used as probe and which metric should be employed to measure their performance.With this respect, it is still questioned if one should rely on simple models <cit.> or complex ones <cit.> in terms of model parametrization.Specifically, voita2020information suggest to design alternative probes using a novel information-theoretic approach which balances the probe inner complexity with its task performance.Concerning works facing the issue of investigating the effectiveness of the probing paradigm, hewitt2019designing observe that probing tasks might conceal the information about the NLM representation behind the ability of the probe to learn surface patterns in the data. To test this idea, they introduced control tasks, a set of tasks that associate word types with random outputs that can be solved by simply learning regularities. Along the same line, ravichander-etal-2021-probing test probing tasks by creating control datasets where a property is always reported in a dataset with the same value, thus it is not discriminative for testing the information contained in the representations. Their experiments highlight that the probe may learn a property also incidentally, thus casting doubts on the effectiveness of probing tasks. The scenario defined by the latter two works is the one we deal with in this paper. Specifically, we introduce a new approach to put increasingly under pressure the effectiveness of a suite of probing tasks to test the linguistic knowledge implicitly encoded by BERT <cit.>, one of the most prominent NLMs. To achieve this goal, we set up a number of experiments (see Section <ref>) aimed at comparing the performance of a regression model trained with BERT representations to predict the values of a set of linguistic properties extracted from the Italian Universal Dependency Treebank <cit.> and from a suite of control datasets we specifically built for the purpose of this study. We define a control dataset as a set of linguistic features whose values were automatically altered in order to be increasingly different from the values in the treebank, referred to as gold values. Our underlying hypothesis is that if the predictions of the increasingly altered values progressively diverge from the predictions of the gold values, this possibly suggests that the corresponding probing tasks are effective strategies to test the linguistic knowledge embedded in BERT representationWe will discuss the results of our experiments in light of this hypothesis in Section <ref>. In Section <ref> we will draw the conclusions. Note that this is one of the few studies focused on non-English NLMs. In fact, with the exception of <cit.>, the majority of research related to interpretability issues is focused on English or, at most, multilingual models. Contributions To the best of our knowledge this is the first paper that (i) introduces a methodology to test the reliability of probing tasks by building control tasks at increasing level of complexity, (ii) puts under pressure the probing approach considering the Italian language.

Conclusion
In this paper we described a methodology to test the effectiveness of a suite of probing tasks for evaluating the linguistic competence encoded by NLMs. To this aim, we analysed the performance of a probing model trained with BERT representations to predict the authentic and automatically altered values of a set of linguistic features derived from IUDT. We observed general higher performance in the prediction of authentic values, thus suggesting that the probing model relies on linguistic competences to predict linguistic properties. However, when we constrained automatically altered values with respect to sentence length, the model tends to learn surface patterns in the data. As a general remark, it should be pointed out that our analyses dealt only with sentences showing a standard length (i.e., between 10 and 30 tokens per sentence). This choice, if on the one hand made our results more directly comparable across bins of sentences sharing the same length, on the other hand excluded from the analyses the shortest and the longest sentences of IUDT. Our future work will be devoted to replicate the probing task experiments described in this paper also on control datasets comprising sentences whose length is outside of the range considered here. To this aim, we performed preliminary analyses to test the scores of probing tasks on gold IUDT sentences that are less than 10-token and more than 30-token long. Interestingly, we noticed that the probing model is less accurate when predicting the linguistic features extracted from the group of IUDT short sentences. Specifically, the average Spearman correlation obtained on such group is 0.47, while probing scores on longer sentences (+30-token long) and on those used in our experiments achieved an average correlation of 0.56 and 0.66 respectively. Starting from this preliminary finding, a possible future investigation could focus on whether using longer or shorter sentences would also have an effect on the probing scores obtained with the control datasets.In future work we also plan to investigate which features are more diagnostic of the linguistic competence encoded by a NLM and which ones, on the contrary, are more influenced by confounders, such as sentence length.