A Tale of a Probe and a Parser (2020)

Abstract: Measuring what linguistic information is encoded in neural models of language has become popular in NLP. Researchers approach this enterprise by training “probes”—supervised models designed to extract linguistic structure from another model’s output. One such probe is the structural probe (Hewitt and Manning, 2019), designed to quantify the extent to which syntactic information is encoded in contextualised word representations. The structural probe has a novel design, unattested in the parsing literature, the precise benefit of which is not immediately obvious. To explore whether syntactic probes would do better to make use of existing techniques, we compare the structural probe to a more traditional parser with an identical lightweight parameterisation. The parser outperforms structural probe on UUAS in seven of nine analysed languages, often by a substantial amount (e.g. by 11.1 points in English). Under a second less common metric, however, there is the opposite trend—the structural probe outperforms the parser. This begs the question: which metric should we prefer?

## introduction recently, unsupervised sentence encoders such as elmo @xcite and bert @xcite have become popular within nlp. these pre-trained models boast impressive performance when used in many language-related tasks, but this gain has come at the cost of interpretability. a natural question to ask, then, is whether these models encode the traditional linguistic structures one might expect, such as part-of-speech tags or dependency trees. to this end, researchers have invested in the design of diagnostic tools commonly referred to as probes @xcite @xcite @xcite . probes are supervised models designed to extract a target linguistic structure from the output representation learned by another model. based on the authors' reading of the probing literature, there is little consensus on where to draw the line between probes and models for performing a target task (e.g. a part-of-speech tagger versus a probe for identifying parts of speech). the main distinction appears to be one of researcher intent: probes are, in essence, a visualisation method @xcite . their goal is not to best the state of the art, but rather to indicate whether certain information is readily available in a model-probes should not "dig" for information, they should just expose what is already present. indeed, a sufficiently expressive probe with enough training data could learn any task @xcite , but this tells us nothing about a representation, so it is beside the point. for this reason, probes are made "simple" @xcite , which usually means they are minimally parameterised. 1 syntactic probes, then, are designed to measure the extent to which a target model encodes syntax. a popular example is the structural probe @xcite , used to compare the syntax that is decodable from different contextualised word embeddings. rather than adopting methodology from the parsing literature, this probe utilises a novel approach for syntax extraction. however, the precise motivation for this novel approach is not immediately clear, since it has nothing to do with model complexity, and appears orthogonal to the goal of a probe. probes are designed to help researchers understand what information exists in a model, and unfamiliar ways of measuring this information may obscure whether we are actually gaining an insight about the representation we wish to examine, or the tool of measurement itself. we observe that the syntactic distance between displeases and everything is 2 (the red path). using the structural probe as a case study, we explore whether there is merit in designing models specifically for the purpose of probing-whether we should distinguish between the fundamental design of probes and models for performing an equivalent task, as opposed to just comparing their simplicity. we pit the structural probe against a simple parser that has the exact same lightweight parameterisation, but instead employs a standard loss function for parsing. experimenting on multiligual bert @xcite , we find that in seven of nine typologically diverse languages studied (arabic, basque, czech, english, finnish, japanese, korean, tamil, and turkish), the parser boosts uuas dramatically; for example, we observe an 11.1-point improvement in english. in addition to using uuas, @xcite also introduce a new metric-correlation of pairwise distance predictions with the gold standard. we find that the structural probe outperforms the more traditional parser substantially in terms of this new metric, but it is unclear why this metric matters more than uuas. in our discussion, we contend that, unless a convincing argument to the contrary is provided, traditional metrics are preferable. justifying metric choice is of central importance for probing, lest we muddy the waters with a preponderance of ill-understood metrics. ## syntactic probing using distance here we introduce syntactic distance, which we will later train a probe to approximate. ## probe, meet parser in this section, we introduce a popular syntactic probe and a more traditional parser. ## results this section presents results for the structural probe and structured perceptron parser. ## discussion: probe v. parser although we agree that probes should be somehow more constrained in their complexity than models designed to perform well on tasks, we see no reason why being a "probe" should necessitate fundamentally different design choices. it seems clear from our results that how you design a probe has a notable effect on the conclusions one might draw about a representation. our parser was trained to recover trees (so it is more attuned to uuas), whilst the structural probe was trained to recover pairwise distances (so it is more attuned to dspr)-viewed this way, our results are not surprising in the least. the fundamental question for probe designers, then, is which metric best captures a linguistic structure believed to be a property of a given representation-in this case, syntactic dependency. we suggest that probing research should focus more explicitly on this question-on the development and justification of probing metrics. once a metric is established and well motivated, a lightweight probe can be developed to determine whether that structure is present in a model. if proposing a new metric, however, the burden of proof lies with the researcher to articulate and demonstrate why it is worthwhile. moreoever, this process of exploring which details a new metric is sensitive to (and comparing with existing metrics) ought not be conflated with an analysis of a particular model (e.g. bert)-it should be clear whether the results enable us to draw conclusions about a model, or about a means of analysing one. for syntactic probing, there is certainly no apriori reason why one should prefer dspr to uuas. if anything, we tentatively recommend uuas, pending further investigation. the dspr p +f w results show no clear difference between the models, whereas uuas exhibits a clear trend in favour of the parser, suggesting that it may be easier to recover pairwise distances from a good estimate of the tree than vice versa. uuas also has the advantage that it is well described in the literature (and, in turn, well understood by the research community). according to uuas, existing methods were able to identify more syntax in bert than the structual probe. in this context, though, we use these results not to give kudos to bert, but to argue that the perceptron-based parser is a better tool for syntactic probing. excluding differences in parameterisation, the line between what constitutes a probe or a model designed for a particular task is awfully thin, and when it comes to syntactic probing, a powerful probe seems to look a lot like a traditional parser. ## conclusion we advocate for the position that, beyond some notion of model complexity, there should be no inherent difference between the design of a probe and a model designed for a corresponding task. we analysed the structural probe @xcite , and showed that a simple parser with an identical lightweight parameterisation was able to identify more syntax in bert in seven of nine compared languages under uuas. however, the structural probe outperformed the parser on a novel metric proposed in @xcite , bringing to attention a broader question: how should one choose metrics for probing? in our discussion, we argued that if one is to propose a new metric, they should clearly justify its usage.