QAN om: Question-Answer driven SRL for Nominalizations (2020)

Abstract: We propose a new semantic scheme for capturing predicate-argument relations for nominalizations, termed QANom. This scheme extends the QA-SRL formalism (He et al., 2015), modeling the relations between nominalizations and their arguments via natural language question-answer pairs. We construct the first QANom dataset using controlled crowdsourcing, analyze its quality and compare it to expertly annotated nominal-SRL annotations, as well as to other QA-driven annotations. In addition, we train a baseline QANom parser for identifying nominalizations and labeling their arguments with question-answer pairs. Finally, we demonstrate the extrinsic utility of our annotations for downstream tasks using both indirect supervision and zero-shot settings.

## introduction semantic role labeling (srl) is the prominent representation for annotating predicate-argument structures. srl annotations were shown useful for various downstream tasks, such as machine comprehension @xcite , cross-document coreference @xcite , dialog @xcite and summarization @xcite . traditionally, srl research is biased toward focusing on verbal predicates, as evident by their dominance in large scale semantic resources, such as propbank @xcite , framenet @xcite and @xcite , and consequentially among srl models @xcite @xcite . nevertheless, other types of predicates, such as nominalizations, are frequent in natural language, which also draw some research attention @xcite @xcite . as a significant milestone, the nombank initiative @xcite provided extensive predicateargument annotations for various types of nouns. in particular, since deverbal nominalizations share an underlying argument structure with their verbal counterparts, nombank annotates these by applying the same annotation scheme as propbank. a verb-derived noun will be mapped to a frame file shared by propbank's verbal predicates, and accordingly a shared role-set. this design principle is meant for converging the semantic role representation of deverbal nominalizations with their corresponding verbal predicates, thus abstracting semantic content over surface realization specifics @xcite . annotating srl resources involves substantial effort and cost. this hinders research progress, as it is hard to extend large-scale resources to additional text genres and languages. in order to address this data collection barrier, question-answer driven semantic role labeling (qa-srl) @xcite was proposed as a natural, easily attainable formulation of srl. qa-srl labels each predicate-argument relationship with a question-answer pair, where natural language questions represent semantic roles, and answers correspond to arguments (see thomas has proved in different ways that god exists, including an argument dubbed "the ontological argument". ## qanom -representation and dataset construction in this section, we describe our proposal for representing predicate-argument information regarding nominalizations. the fundamental rationale is that verb-derived nouns commonly share the argument structure of their verb counterpart. 4 hence, it is natural to interrogate their arguments with verb-headed questions, much the same way qa-srl captures verbal arguments. as an example, consider the two sentences in the equivalent proven event. consequentially, they are naturally captured by the same role labels, both for propbank and nombank, and analogously for qa-srl and qanom, resulting in a unified representation that can facilitate abstraction and generalization in downstream tasks @xcite . ## baseline parser for qanom in this section, we present an initial qanom parser, to serve as baseline for future work on this task. we apply a simple pipeline corresponding qanom annotation sub-tasks, namely isverbal and qasrl. given a sentence, a predicate detector classifies nominalization candidates (extracted using our lexical-based procedure) as verbal vs. non-verbal. we developed a vanilla bert-based model implemented by fine-tuning bert-base-cased pre-trained model @xcite ) on qanom's isver-bal task. contextualized representations of candidates are classified by a binary classification layer. positive (i.e. verbal) nominalization predicates are then passed on to a qa-srl parser, for which we simply adopt and re-train the current state-of-the-art qa-srl parser @xcite for the qanom data. their model is an argument-first pipeline. a span-based argument detector first makes independent binary decisions for all o(n 2 ) spans in the sentence, selecting arguments passing a threshold τ . then, a representation of each selected span is passed into the question generation model, which uses an lstm decoder to sequentially predicts the 7 slots which comprise a qa-srl question. both models leverage contextualized representations to encode the sentence or the argument span. ## extrinsic evaluation of qanom there are two main paths for utilizing explicit semantic representations downstream: (1) utilizing the semantic signal they provide to enhance language understanding within learned models, through additional training; (2) using them as an explicit intermediate structure over the text, which enables applying more controlled algorithms over this structure. in subsections §6.1 and §6.2, we evaluate the downstream utility of the qanom dataset, with respect to these two paths. ## conclusion we present qanom, a light-weight qa-based scheme for annotating semantic roles for deverbal nominalizations. qanom shares its role space with the promising qa-srl line of work, is attainable through a simple crowdsourcing task, and is shown to contain valuable information, both compared to expert-annotated nominal srl resources and when extrinsically evaluated on nominalization-related tasks. while we present baseline figures for qanom parsing, future work should explore how to improve model performance, considering e.g. joint learning or transfer techniques @xcite . wh aux subj verb obj1 prep obj2\misc ? 1 who has proven something ? 2 how did someone prove something ? 3 what has someone proven ? 4 what has been proven by someone ? 5 who did someone prove something about ? 6 what did someone prove about someone ? diana trandabȃt ¸. 2011. using semantic roles to improve summaries. in proceedings of the 13th european workshop on natural language generation, pages 164-169. association for computational linguistics. christopher walker, stephanie strassel, julie medero, and kazuaki maeda. 2006. ace 2005 multilingual training corpus. linguistic data consortium, philadelphia, 57:45. hai wang, mohit bansal, kevin gimpel, and david mcallester. 2015. machine comprehension with syntax, frames, and semantics. in proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (volume 2: short papers), volume 2, pages 700-706. yanpeng zhao and ivan titov. 2020. unsupervised transfer of semantic role models from verbal to nominal domain. arxiv preprint arxiv:2005.00278.