PISA : A measure of Preference In Selection of Arguments to model verb argument recoverability (2020)

Abstract: Our paper offers a computational model of the semantic recoverability of verb arguments, tested in particular on direct objects and Instruments. Our fully distributional model is intended to improve on older taxonomy-based models, which require a lexicon in addition to the training corpus. We computed the selectional preferences of 99 transitive verbs and 173 Instrument verbs as the mean value of the pairwise cosines between their arguments (a weighted mean between all the arguments, or an unweighted mean with the topmost k arguments). Results show that our model can predict the recoverability of objects and Instruments, providing a similar result to that of taxonomy-based models but at a much cheaper computational cost.

## introduction verb meaning, together with pragmatic and discourse-related factors, has long been identified as playing a major role in determining argument optionality @xcite . here, we specifically focus on the semantic recoverability of arguments, which has often been claimed to be the key determinant of object omission @xcite @xcite @xcite @xcite . @xcite link argument recoverability to selectional preferences by means of an experiment we detail in section 2, and also show that it is correlated with plausibility and typicality judgments provided by human subjects. the relation linking a verb to its optional argument is a grammatical function in @xcite , such as "subject" or "direct object", but it may also be a semantic role, such as "instrument" or "patient". the choice between the two depends on computational requirements rather than on theoretical constraints. let us consider some examples. recoverability affects the grammaticality of the sentences in (1), since the patient of an eating event (as in ( 1a )) is easily recoverable as a member of the class of edibles, but there are no such strict constraints on what one can make (as in (1b)). the same applies to prepositional phrases filling the instrument role, albeit in a more nuanced fashion. @xcite @xcite have shown that verbs describing actions may be divided into two classes based on whether they semantically require an instrument (as in (2a)) or they merely allow it (as in ( 2b )). require-instrument verbs select for a smaller range of instruments than allow-instrument verbs: for instance, a beheading event is very likely to involve a heavy bladed tool like a sword, while a killing event may happen with a much larger set of tools or even without any. while both sentences in (2) are grammatically acceptable, given that instruments are syntactic adjuncts (rissman and rawlins, 2017), it is much easier to infer the type of instrument used in the event depicted in (2a) than the one in (2b), given the high recoverability of the tool used in the former from verb meaning. instruments are an interesting object of study for a number of reasons. first of all, they are arguably underesearched with respect to recoverability if compared to direct objects, and to our knowledge there are no computational models of instrument recoverability. moreover, this semantic role is usu-ally overtly realized with prepositional phrases in english and typologically similar languages, making it very different from direct objects and a computationally challenging problem to tackle. the first attempt to formalize the requirement that a verbal dependent be recoverable in order to be omitted was provided by @xcite ). resnik's result is a probabilistic information-theoretic model of selectional constraints relying on a manuallybuilt lexicon. in this paper, we propose a fully distributional model of argument recoverability. the crucial idea is that the more mutually similar the arguments selected by a verb are, the more recoverable they are from the meaning of the verb alone. this intuition builds upon the hypotheses and results by @xcite ; @xcite , adding a distributional dimension that those works lacked. we will show that this solution reproduces resnik's findings about direct objects, addresses the drawbacks of resnik's model, and is robust enough to predict the recoverability of instruments. a model such as ours can find a wide range of applications, both in natural language processing (e.g., in semantic role labeling and word sense disambiguation), in linguistic research (e.g., language acquisition and processing), and even in real-life technology (e.g., to provide robots with commonsense knowledge). ## related work given a verb-relation pair (such as the verb-object relation) @xcite formulate the selectional preference strength (sps) of the verb with respect to the possible fillers in the given role (see eq. 1) as the kullback-leibler divergence between the (posterior) distribution of wordnet synsets for the given verb-relation pair and the (prior) distribution of synsets participating in the given relation over all verbs in the corpus. in 1, "classes" refers to the taxonomic classes used in wordnet: resnik's work inspired more taxonomy-based models of sa over the years @xcite @xcite @xcite , but no further refinements of the sps itself. distributional semantic models (dsms) @xcite instead tackle the main drawback of taxonomy-based models, i.e. the need for a manually-built lexicon, by requiring no other resource than the corpus they are trained on. they rely on different strategies to compute sa, such as clustering @xcite , support vector machines @xcite , and hybrid approaches (schulte im @xcite . @xcite and @xcite provide a cognitively plausible distributional model that proves particularly relevant for our purposes. given a verb-relation pair, it computes the plausibility of a potential argument of the pair (i.e., the sa of the triple) via the weighted similarity between that argument and the exemplar arguments stored in the model as vectors, as shown in eq. 3: ## pisa: a novel measure of preference in selection of arguments we introduce pisa, our own distributional measure of preference in selection of arguments, which we use to model argument recoverability in the spirit of resnik's sps. it stems from the intuition that the vector-based sps of a given verbrelation pair should be positively correlated with the distributional similarity of their arguments. the simplest way to capture this notion is by computing the semantic density of the verb-relation pair as the mean value of the pairwise cosine similarity between the arguments of the pair. in order to take into account the fact that some arguments are more associated with a given verb-relation pair than others, it is possible to compute a weighted measure of semantic density. this is tantamount to averaging @xcite 's sa in eq. 3 over n arguments of a given verb-relation pair: as in previous literature, relations in our model may be syntactic ones or semantic roles, depending on their availability in a corpus. we used only one similarity measure, cosine. ## experimental settings the datasets and the scripts we used to run our model are freely available on github foot_1 .