Unsupervised Paraphrasability Prediction for Compound Nominalizations (2022)

Abstract: Commonly found in academic and formal texts, a nominalization uses a deverbal noun to describe an event associated with its corresponding verb. Nominalizations can be difficult to interpret because of ambiguous semantic relations between the deverbal noun and its arguments. Automatic generation of clausal paraphrases for nominalizations can help disambiguate their meaning. However, previous work has not identified cases where it is awkward or impossible to paraphrase a compound nominalization. This paper investigates unsupervised prediction of paraphrasability, which determines whether the prenominal modifier of a nominalization can be re-written as a noun or adverb in a clausal paraphrase. We adopt the approach of overgenerating candidate paraphrases followed by candidate ranking with a neural language model. In experiments on an English dataset, we show that features from an Abstract Meaning Representation graph lead to statistically significant improvement in both paraphrasability prediction and paraphrase generation.

## introduction a nominalization is a noun (e.g., "response") that is morphologically derived from a verb ("respond"), and that designates some aspects of the event referred to by the verb @xcite . in a compound nominalization, this deverbal noun may have both prenominal and postnominal modifiers. the prenominal modifier can be a noun (e.g., "police response to the rioting") or an adjective ("bodily injury to a friend"), while postnominal modifiers are prepositional phrases ("presidential nomination of harrison"). academic and other formal texts utilize nominalization extensively to produce a compact and abstract writing style. the meaning of compound nominalizations can however be difficult to interpret because of ambiguous semantic relations be-tween the deverbal noun and its modifiers. in particular, the prenominal modifier can play multiple semantic roles in the corresponding predicate or clausal paraphrase: as a subject (e.g., "the police response" → "the police responds"); as an object ("bodily injury" → "injure the body"); as an oblique ("presidential nomination" → "nominate as president"; as an adverb ("symbolic admission" → "admit symbolically"; or none of the above ("stellar performance" → "a star performs"). the paraphrasability of the prenominal modifier -whether it describes an entity, the manner of an action, or neither -therefore has direct impact on nlp tasks that require interpretation of compound nominalizations. this ambiguity affects accuracy in relation extraction, which is important for information retrieval and question answering @xcite . a machine translation system must also be able to render the deverbal noun and its prenominal modifier properly when there is no equivalent nominalization in the target language. further, paraphrasability prediction could benefit nominal semantic role labeling, which needs to identify the role played by the prenominal modifier @xcite @xcite . finally, it is critical for nominalization paraphrasing. when a clausal paraphrase is not available for the input nominalization, approaches that do not consider paraphrasability may produce an invalid or misleading output @xcite . this study focuses on english, the dominant language for academic texts. it aims to make two contributions. first, we enlarge an existing dataset to cover three paraphrasability categories for prenominal modifiers in a compound nominalization (paraphrased as noun, as adverb or nonparaphrasable). second, we extend an algorithm to take paraphrasability into account, and show that features from abstract meaning representation graphs improve performance in both paraphrasabil-ity prediction and paraphrase generation. the rest of the paper is organized as follows. after defining our task (section 2), we summarize previous research (section 3). we then describe our dataset (section 4) and present our approach (sections 5-6). then, we discuss experimental results (section 7-8) and conclude (section 9). ## dataset the only publicly available dataset of clausal paraphrases, developed by @xcite , provides 450 paraphrases for english nominalizations. all paraphrasability adjectival nominal label modifier modifier noun 281 169 adverb 55 n/a nil 91 38 ## paraphrase candidates our approach is to first overgenerate paraphrase candidates for each input, and then identify the op-1 accessible at https://github.com/nominalizationparaphrase timal candidate. this section presents the candidate types, and the next section describes the candidate selection algorithm. ## approach as discussed in section 4.1, the input is a sentence that contains a nominalization, headed by a deverbal noun that has a prenominal modifier and a prepositional phrase ( we first overgenerate paraphrases to construct a candidate pool (section 6.1), and then filter the pool by considering paraphrasability (section 6.2). for the paraphrase generation task, the output is the best candidate selected by the textual entailment and language models (section 6.3). for the paraphrarasability prediction task, the output is the paraphrasability label associated with the selected candidate ( ## results ## conclusion a clausal paraphrase can help disambiguate a nominalization semantically, especially when the prenominal modifier is difficult to interpret. this paper has presented the first study on determining the paraphrasability of the prenominal modifier in a compound nominalization. we have expanded an existing dataset to cover cases when the prenominal modifier can appear as a noun in the paraphrase, as an adverb, or not at all. our experiments suggest that overgeneration of paraphrase candidates, followed by ranking with a textual entailment model and language model, can yield competitive results. further, amr-based features lead to statistically significant improvement in performance. a limitation of our study is the restricted syntactic form of the input nominalizations. to facilitate more comprehensive evaluation, future research should consider expanding the dataset further to cover a wider range of nominalizations, and richer variations in their clausal paraphrases.