Automatic Nominalization of Clauses through Textual Entailment (2022)

Abstract: Nominalization re-writes a clause as a noun phrase. It requires the transformation of the head verb of the clause into a deverbal noun, and the verb’s modifiers into nominal modifiers. Past research has focused on the selection of deverbal nouns, but has paid less attention to predicting the word positions and word forms for the nominal modifiers. We propose the use of a textual entailment model for clause nominalization. We obtained the best performance by fine-tuning a textual entailment model on this task, outperforming a number of unsupervised approaches using language model scores from a state-of-the-art neural language model.

## introduction many textbooks on academic writing devote significant attention to nominalization, which lends a more abstract, concise and objective tone to a text (kamler and thomson, 2006; bailey, 2011). since nominalization requires careful lexical selection and clause restructuring, it demands advanced vocabulary knowledge and grammatical skills, making it challenging even for many human writers. past research on clause nominalization has concentrated on replacement of the head verb with a deverbal noun (e.g. 'omits' → 'omission') and resource development to support the task (meyers et al., 1998; habash and dorr, 2003; saberi and lee, 2019). less attention has been paid to the clause restructuring that is required for transforming the verb's modifiers in the clause to nominal arguments, including word reordering (e.g., postposing the subject 'arabic', this paper investigates methods for clause nominalization that optimize the position and pos of the nominal arguments. we focus on clauses headed by a verb with up to three syntactic arguments, including subjects, objects, adjectival phrases or prepositional phrases. the output is a nominalized form of the clause that preserves its original meaning. experimental results show that a textual entailment model that is fine-tuned on this task can outperform unsupervised approaches based on neural language model scores. the rest of the paper is organized as follows. after a review of previous work (section 2), we present our two-step algorithm: candidate generation (section 3) followed by candidate ranking (section 4). finally, we report experimental results (section 6). our task is distinct from semantic role labeling (lapata, 2002; padó et al., 2008), since it must take into account both the fluency of the nominalization and its semantic equivalence to the original clause. another related task, the paraphrasing of nominalizations, can be viewed as the reverse of ours. in an unsupervised approach, lee et al. (2021) selected the best clausal paraphrase of a nominalization using a language model and a textual entailment model, which has also been applied to other nlp tasks such as question answering (trivedi et al., 2019), summarization (kryściński et al., 2020), and relation extraction (sainz et al., 2021). however, the candidate generation algorithm for our task is significantly different. we also address a wider range of nominalization patterns in our evaluation, and show that a supervised approach can improve performance. ## nominalization generation as shown in ## data since our research focus is at the clause level rather than the re-writing of the entire sentence, we targeted sentences that permit straightforward alternation between a clause and a noun phrase through: (1) a change of conjunction, e.g., "although ⟨clause⟩" ↔ "despite ⟨np⟩"; ## conclusion this paper has reported the first quantitative evaluation on automatic clause nominalization and contributed the first dataset for this task. we have shown that a fine-tuned textual entailment model, followed with reranking with a language model, outperforms a number of competitive unsupervised approaches. in future work, we plan to extend our algorithm to determine whether a target clause can or should be nominalized, and to explore a richer variety of nominalization types.