Venue: NAACL 2022

# Unsupervised Paraphrasability Prediction for Compound Nominalizations

John S. Y. Lee, Ho Hung Lim

Department of Linguistics and Translation

City University of Hong Kong

Hong Kong SAR, China

jsylee@cityu.edu.hk

hhlim3@cityu.edu.hk

Carol Webster

College of Professional and

Continuing Education

The Hong Kong Polytechnic University

Hong Kong SAR, China

carol.webster@cpce-polyu.edu.hk

###### 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 paraphrashidity, 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.

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Footnote †: _Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies_, pages 3254 - 3263

July 10-15, 2022 ©2022 Association for Computational Linguistics

## 1 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 Quirk et al. (1985). In a compound nominalization, this deverbal noun may have both pronemnimal and postnominal modifiers. The prenominal modifier can be a noun (e.g., "_police_ response to the rioting") or an adjective ("_bolily_ 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 between 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 _policer_ response" \(\rightarrow\) "the _police_ responds"); as an object ("_boldiy_ injury" \(\rightarrow\) "injure the _body_"); as an oblique ("_presidential_ nomination" \(\rightarrow\) "nominate _as president_"; as an adverb ("_symbolic_ admission" \(\rightarrow\) "admit _symbolically_"; or none of the above ("_stellar_ performance" \(\neq\) "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 Greenwood (2004); Klein et al. (2020). 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 Lapata (2002); Pado et al. (2008); Kilicoglu et al. (2010). Finally, it is critical for nomalization 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 Lee et al. (2021).

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 nonparaphrsable). 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).

## 2 Paraphrasability of nominalizations

### Motivation

Our goal is to paraphrase a nominalization into a "clausal paraphrase", which we define as a clause headed by a verb whose syntactic arguments (e.g., subject, object, and prepositional object) are transformed from the nominal arguments in the input nominalization. We focus on compound nominalizations in which the head noun has a prenominal modifier and a prepositional object, following the syntactic form targeted by the only publicly available dataset for nominalization paraphrasing Lee et al. (2021). Some example inputs and outputs are shown in Table 1.

A clausal paraphrase would not be possible for a compound nominalization if there is no suitable verb equivalent for its head noun. Large-scale language resources Meyers et al. (1998) already exist to help determine whether such a verb exists, and the task has been tackled in the context of QA semantic role labeling for nominalization Klein et al. (2020). Less attention has been paid to another factor, namely, whether the prenominal modifier can be expressed as a subject, object or prepositional object in the paraphrase. We are not aware of previous data-driven research on this task, which is the focus of this paper. We will not consider the prepositional object in the input nominalization, since it can be incorporated into a clausal paraphrase in most cases.

### Task definition

The term "paraphrasability" has been used for the degree of semantic equivalence between syntactic variants of predicate phrases Fujita and Sato (2008). We will use this term to refer to the three categories of paraphrasing behavior of prenominal modifiers in compound nominalizations:

**Noun**: The prenominal modifier is a noun, or is an adjective that _pertains_ to a noun, that can serve as the subject, object or prepositional object in a clausal paraphrase. In other words, either the prenominal modifier itself (e.g., "police") or its _pertainym_ ("president" for "presidential") literally refers to the entity that participates in the event denoted by the deverbal noun ("_policce_ response", "_presidential_ nomination").
**Adverb**: The prenominal modifier is an adjective that can appear in the clausal paraphrase in its adverbial form (e.g., "_frontal_ opposition" \(\rightarrow\) "oppose _frontally_"), but not as pertainym ("_frontal_ opposition" \(\not\to\) "the _front_ opposes").
**Nil**: The prenominal modifier cannot be paraphrased with either method above (e.g., "_stellar_ performance" \(\not\to\) "_a star_ performs"; "_brain_ drain" \(\not\to\) "drain a _brain_").

As shown in Table 1, the input is a nominalization that consists of a deverbal noun (derived from the verb \(V\)); its prenominal modifier (bolded); and a prepositional phrase. The output of the **Paraphrasability Prediction** task is the part-of-speech label of the word to which the prenominal modifier is paraphrased (bolded). The label can be Noun, Adverb, or Nil when it is not paraphrsable.

The output of the **Paraphrase Generation** task is a clausal paraphrase of the input. It incorporates the verb \(V\), the prepositional object from the input (marked with \(O\)); and either a noun (marked with \(M\)) or an adverb (marked with \(B\)) corresponding to the prenominal modifier. The gold paraphrase of the NiI type input is defined as _null_. The only way to render such an input as a clause is with a support verb or light verb (e.g., "stellar performance" \(\to\) "_give_ a stellar performance" ). Since the paraphrase retains the original nominalization, it does not serve our goal of unpacking its meaning.

## 3 Previous work

### Noun literality prediction

There has been extensive research on compositionality analysis on noun compounds Reddy et al. (2011), adjective-noun combinations and other types of multiword expressions (MWEs) Biemann and Giesbrecht (2011); Ramisch et al. (2016); Cordeiro et al. (2019); Jana et al. (2019). Compositionality refers to the extent to which the meaning of the MWE can be expressed in terms of the meaning of its constituents. It therefore has considerable overlap with literary prediction, which would identify, for example, the noun "rat" in "_rat_ race" as non-literal Reddy et al. (2011) 

Our task is closely related to literality prediction since compound nominalizations are a subset of noun-noun compounds; in particular, a prenominal modifier that is "literal" would likely be of paraphrasability type Noun (Section 2.2). We will therefore evaluate the performance of a state-of-the-art noun literality prediction model Shwartz and Dagan (2019) in our experiment.

Our task is nonetheless distinct from literally prediction since it focuses on paraphrasability rather than literalness. For example, even when a prenominal modifier is used metaphorically and is non-literal (e.g. "_circular_ argument"), it would be labeled Noun in terms of paraphrasability if it can appear in a clausal paraphrase ("argue in a _circle_").

### Noun compound interpretation

A noun compound can be disambiguated with a free-form paraphrase Hendrickx et al. (2013), or with verbs and prepositions linking the two nouns, e.g., "apple pie" is a "_pie_ with _apples_" Butnariu et al. (2010); Nakov and Hearst (2013). Unsupervised approaches have been found to be effective for noun compound interpretation. Paraphrase templates with slots for prepositions and predicates, for example, can be filled using pre-trained masked language models Ponkiya et al. (2020). We will likewise investigate unsupervised approaches in this work. Even though compound nominalizations are a subset of noun-noun compounds, our task is different since paraphrases in noun compound interpretation do not transform the head noun into a verb.

### Paraphrasing nominalizations

Research on nominalization interpretation has mostly focused on nominal semantic role labeling, which assigns abstract labels (e.g., agent, patient, manner) to arguments of nominalizations Lapata (2002); Nicholson and Baldwin (2008); Pado et al. (2008); Kilicoglu et al. (2010). Given the systematic correspondences between nominalization and clause structure, there have also been efforts to paraphrase nominalizations as clauses. Algorithms have been proposed for automatic acquisition of paraphrase templates, which can cover nominalization inputs Shinyama et al. (2002). The paraphrasing task has also been indirectly addressed in a model for question and answer generation from nominalizations Klein et al. (2020).

The most closely related work to this paper was reported in Lee et al. (2021). Their proposed model first overgenerates paraphrase candidates, and then uses textual entailment to identify the optimal candidate. However, since all nominalizations in their dataset have paraphrases, their algorithm makes no judgment on paraphrasability. We extend their dataset and investigate paraphrasability prediction to fill in this research gap.

## 4 Dataset

The only publicly available dataset of clausal paraphrases, developed by Lee et al. (2021), provides 450 paraphrases for English nominalizations. All

\begin{table}
\begin{tabular}{l|l|l} \hline Input & Paraphrase & Paraphrashility \\ Nominalization & Generation & Prediction \\ \hline
**American** influence on global culture & **America\({}^{M}\) influences\({}^{V}\) global culture\({}^{O}\)** & Noun \\
**police** response to the rioting & the **police\({}^{M}\) responds\({}^{V}\)** to the rioting\({}^{O}\)** & \\ \hline
**climatic** effects of air pollutants & air pollutants\({}^{O}\) affect\({}^{V}\) the **climate\({}^{M}\)** & \\
**war** preparations of the government & the government\({}^{O}\) prepares\({}^{V}\) for **war\({}^{M}\)** & \\ \hline
**bodily** injury to a friend & injure\({}^{V}\) the **body\({}^{M}\)** of a friend\({}^{O}\)** & \\
**student** admissions into universities & admit\({}^{V}\)**students\({}^{M}\)** into universities\({}^{O}\) & \\ \hline
**presidential** nomination of Harrison & nominate\({}^{V}\) Harrison\({}^{O}\) as **president\({}^{M}\)** & \\
**business** travel to Greece & travel\({}^{V}\) to Greece\({}^{O}\) for **business\({}^{M}\)** & \\ \hline
**naval** assistance from Italian powers & **navis\({}^{M}\)** of Italian powers\({}^{O}\) assist\({}^{V}\)** & \\
**majority** decision of the panel & a **majority\({}^{M}\)** of the panel\({}^{O}\) decides\({}^{V}\) & \\ \hline
**frontal** opposition of the employers & The employers\({}^{O}\) oppose\({}^{V}\)**fontally\({}^{B}\)** & Adverb \\
**dynamic** allocation of licenses & allocate\({}^{V}\) licensees\({}^{O}\)**dynamically\({}^{B}\)** & \\ \hline
**stellar** performance of the rookies & _null_ & Nil \\
**brain** drain of scientists & _null_ & \\ \hline \end{tabular}
\end{table}
Table 1: Input and output of the **Paraphrasability Prediction** and **Paraphrase Generation** task (Section 2.2) 

instances are of the paraphrasability label Noun (Section2.2). To facilitate our study, we enlarged this dataset with inputs of the Adverb and Nil paraphrasability labels.

### Data source

In the interest of consistency with the existing dataset, we focus on nominalizations with the same syntactic pattern. Specifically, we collected sentences from English Wikipedia that contain a noun phrase headed by a deverbal noun with one prenominal modifier and one postnominal modifier.

As shown in Table1, the postnominal modifier is a prepositional phrase with prepositional object \(O\). The prenominal modifier can be a noun or an adjective. To create a challenging dataset, the adjective must have a pertainym, or can itself also serve as noun (e.g., "light"), such that multiple paraphrasability labels are plausible.

### Annotation

Two annotators, a native speaker and a near-native speaker of English, independently classified the nominalizations into one of three paraphrasability labels (Section2.2). For those labeled as Adverb, the annotator composed a paraphrase with an adverb that is derivationally related to the adjective. For those labeled as Nil, no paraphrase was required. Examples for each label are provided in Table1.

A professor of linguistics who is a native speaker of English reviewed the annotation, either keeping both or selecting one of them. A total of 184 non-Noun instances were collected and added to the dataset, resulting in an expanded dataset with 634 paraphrases (Table2).1

Footnote 1: Accessible at [https://github.com/NominalizationParaphrase](https://github.com/NominalizationParaphrase)

## 5 Paraphrase candidates

Our approach is to first overgenerate paraphrase candidates for each input, and then identify the optimal candidate. This section presents the candidate types, and the next section describes the candidate selection algorithm.

Table3 shows the paraphrase candidates for the input "_frontal_ opposition of the employers". The prenominal modifier, "frontal", is transformed into various parts-of-speech and placed at different positions in the candidates. Each candidate is associated with one of the three paraphrasability types:

### Noun

The Noun type candidates are the five paraphrases defined in Lee et al. (2021). The prenominal modifier is paraphrased as a noun in the subject (MVO, MOV), object (OVM, VMO), and oblique (VOM) positions.

### Adverb

There are four paraphrase candidates for the Adverb type. The prenominal modifier must be an adjective. It is paraphrased as an adverb that pertains to itself, according to WordNet Fellbaum (2010); or an adverb that is derivationally related to itself, according to CatVar Habash and Dorr (2003). The adverb (B) is placed either before the verb (BVO, OBV) or at the end of the clause (VOB, OVB).

### Nil

There are, by definition, no obvious paraphrase candidates to represent inputs of the Nil type. We implemented the following alternatives:

**Identity**: The normalization input itself.
**Light verb**: The paraphrase retains the nominalization as the object of a light verb or support verb Grefenstette and Teufel (1995). One paraphrase candidate prepends the light verb; e.g., "home run against Arizona" \(\rightarrow\) "_hit_ a home run against Arizona". The other candidate uses the prepositional object (\(O\)) as subject; e.g., "stellar performance of the rookies" \(\rightarrow\) "the rookies _give_ a stellar performance".
**Predicative adjective**: Limited to prenominal modifiers that are adjectives, this paraphrase uses the adjective predicatively to form a clause. The paraphrase is designed, on the one hand, to be acceptable for Nil type inputs, e.g., "_stellar_ performance of the rookies \(\rightarrow\) "the performance of the rookies is _stellar_"; and on the other hand, to be unacceptable for

\begin{table}
\begin{tabular}{l|c|c} \hline Paraphrasability Label & Adjectivial modifier & Nominal modifier \\ \hline Noun & 281 & 169 \\ Adverb & 55 & n/a \\ Nil & 91 & 38 \\ \hline \end{tabular}
\end{table}
Table 2: Breakdown of our dataset according to paraphrasability label and part-of-speech of the prenominal modifier 

nominal adjectives, which cannot be used as a predicate (Coates, 1971), e.g., "_presidential_ nomination of Harrison" \(\not\rightarrow\) "the nomination of Harrison is _presidential_".

We used a masked language model, BERT Devlin et al. (2019), to generate the most likely determiners, prepositions and light verbs for the paraphrase candidates above.

## 6 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 (Table 1). The prenominal modifier may be a noun or an adjective.

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 (Table 3).

### Candidate pool construction

This step constructs a pool of paraphrase candidates. We evaluated the following methods:

**All**: Include all paraphrase candidates in Table 3.
**Gold**: Include only those paraphrase candidates associated with the gold paraphrasability label.
**Majority baseline**: Include only those paraphrase candidates associated with the majority paraphrasability label, which is Noun in our dataset. This baseline replicates the algorithm proposed by Lee et al. (2021), which considers only the MVO, OVM, VMO, VOM, and MOV paraphrases in Table 3.
**Word frequency baseline**: Include the Noun type (Adverb type) paraphrase candidates only when the noun (the adverb) corresponding to the prenominal modifier has high frequency. The frequency threshold is optimized on our dataset based on frequency statistics in the Google Web 1T N-gram Corpus Brants and Franz (2006).

### Candidate pool filtering

This step filters the candidate pool constructed by the **All** model. We evaluated two methods that consider paraphrasability through semantic parsing and literality prediction, respectively.

#### 6.2.1 Filtering with AMR

Abstract Meaning Representation (AMR) abstracts away from the syntactic realization of a sentence and expresses its meaning with a directed acyclic graph, where nodes represent events and concepts, and edges represent relationships between

\begin{table}
\begin{tabular}{l|p{85.4pt}|p{113.8pt}} \hline \hline Paraphrasability Label & Paraphrase Candidate & Word Order \\ \hline Noun & the _front_ opposes the employers & MVO \\  & the employers oppose the _front_ & OVM \\  & oppose the _front_ of the employers & VMO \\  & opposite the employers at the _front_ & VOM \\  & _front_ employers oppose & MOV \\ \hline Adverb & oppose the employers _frontally_ & VOB \\  & _frontally_ oppose the employers & BVO \\  & the employers oppose _fronally_ & OVB \\  & the employers _frontally_ oppose & OBV \\ \hline Nil & _frontal_ opposition of the employers & Identity \\  & Opposition of the employers is _frontal_ & Predicative Advjective \\  & have _frontal_ opposition of the employers & Light Verb \\  & the employers face _frontal_ opposition & Light Verb \\ \hline \end{tabular}
\end{table}
Table 3: Automatically generated paraphrase candidates (Section 5) for the input “frontal opposition of the employers”, which place the prenominal modifier (M or B), verb (V) and prepositional object (O) in the input nominalization in different word orders 

the nodes (Banarescu et al., 2013). In an AMR graph, deverbal nouns are annotated as verbs, and adjectives pertaining to nouns are annotated in their nominal form whenever possible.

We use PERIN (Samuel and Straka, 2020) to construct an AMR graph for the input sentence, and align the nodes with the words in the input. We will refer to the node aligned to the deverbal noun as the "deverbal noun node"; and the node aligned to the prenominal modifier as the "prenominal modifier node". In Figure 1, the "constitution" node is the prenominal modifier node (aligned to "constitutional" in the input); and the "interpret-01" node is the deverbal noun node (aligned to "interpretation").

The **All+AMR** model predicts Noun as paraphrasability label and removes all non-Noun type candidates from the pool if the prenominal modifier node:

* is an argument of the deverbal noun node; or
* is the domain of the deverbal noun node, and is annotated as a noun.

Otherwise, it predicts paraphrasability to be non-Noun and removes all Noun type candidates from the pool.

For example, the model predicts Noun as paraphrasability label for the sentence in Figure 1, since the prenominal modifier node ("constitution") serves as arg0 to the deverbal noun node ("interpreted-01"). The model rejects Noun as paraphrasability label for the sentence in Figure 2. Even though the prenominal modifier node ("secular") is the domain of the deverbal noun node ("celebrate-02"), it is annotated with the original adjective rather than its nominal form.

#### 6.2.2 Filtering with noun literality prediction

Noun literality prediction is closely related to paraphrasability prediction (Section 3.1). We use LexComp (Shwartz and Dagan, 2019), a state-of-the-art model in noun literality prediction, which is trained on datasets from Reddy et al. (2011) and Tratz et al. (2010) using contextualized word embeddings.2

Footnote 2: [https://github.com/vered1986/lexcomp](https://github.com/vered1986/lexcomp)

Given the prenominal modifier and the deverbal noun in the input, the **All+LexComp** model predicts Noun as paraphrasability label if LexComp predicts "literal", and removes all non-Noun type candidates. Otherwise, it predicts Nil and keeps only the Nil type paraphrases. This model does not perform filtering on an input with an adjectival modifier.

### Candidate selection

A textual entailment model (TE), enhanced with re-ranking by language model scores, was found to yield the strongest performance in paraphrase generation for compound nominalizations (Lee et al., 2021). Taking the nomalization input as the

Figure 1: AMR graph of the sentence “... a court for the _constitutional_ interpretation of law”, which is predicted as Noun by the All+AMR model (Section 6.2.1)

Figure 2: AMR graph of the sentence “... the _secular_ celebration of Christmas”, which is predicted as Non-Noun by the All+AMR model (Section 6.2.1)



premise and a paraphrase candidate as the hypothesis, the TE model predicts whether the facts in the former imply those in the latter. Among the three candidate paraphrases that yield the highest TE scores, the candidate with the highest language model (LM) score is selected.

We replicate this algorithm and apply it on the filtered candidate pool. We use the AllenNLP textual entailment model3, and the log-probability score based on GPT-2 (117M) as the LM score (Salazar et al., 2020).4

Footnote 3: [https://demo.allennlp.org/textual-entailment/roberta-snli](https://demo.allennlp.org/textual-entailment/roberta-snli)

Footnote 4: [https://github.com/awslabs/mlm-scoring](https://github.com/awslabs/mlm-scoring)

## 7 Experimental set-up and metrics

The entire dataset was used for evaluation since our approach is unsupervised. We used SpaCy Honnibal and Johnson (2015) for POS tagging to determine the POS of the prenominal modifier.

_Paraphrasability prediction._ We report precision, recall and F\({}_{1}\). Precision is defined as the number of actual Noun instances, out of all instances predicted by the system as Noun. Recall is defined as the proportion of gold Noun instances that are correctly identified by the system as Noun.

_Paraphrase generation._ We report "paraphrase accuracy" and "word order accuracy" as defined in Lee et al. (2021). For the former, the determiners are removed from all paraphrases. The system is considered correct if the lemmatized form of all words in the predicted paraphrase are identical with those in the gold paraphrase. The latter is defined likewise, except that prepositions are not taken into consideration. It thus essentially measures the system's ability to predict the verb and arguments and to put them into the correct word order. The word orders VOB/BVO and OVB/OBV are considered interchangeable.

## 8 Results

Table 4 shows system performance on the paraphrasability prediction (Section 8.1) and its effect on paraphrase generation (Section 8.2).

### Paraphrasability prediction

Given the preponderance of the Noun label in our dataset, the Majority baseline produced a strong performance at 0.673 precision and perfect recall. It outperforms the Word Frequency baseline, which has slightly higher precision (0.686) but lower recall (0.911), both in terms of F\({}_{1}\) and accuracy.

Using all paraphrase candidates resulted in an improvement in binary classification accuracy (0.711 vs. 0.673) over the Majority baseline, demonstrating the effectiveness of the Adverb and Nil paraphrases (Section 5.2-5.3). In terms of three-way classification, however, it offered no improvement over the Majority baseline (0.671 vs. 0.673). This indicates that while the candidate selection method (Section 6.3) can correctly detect some Noun type candidates as inappropriate, it is less competent in judging between Adverb vs. Nil paraphrases.

The All+LexComp model raised the accuracy by only 0.4% in comparison to the All model. This result suggests that noun literality prediction is only slightly helpful as a proxy for paraphrasability.

The All+AMR model achieved the highest F\({}_{1}\) (0.852) by raising both the precision and recall of the All model. The improvement is statistically significant in terms of both binary classification (0.782)5 and three-way classification (0.744)6. These results show that AMR is useful for predicting paraphrasability, which may be due to the more fine-grained semantic information in the AMR graphs that could not be inferred by the LM and TE models in the candidate selection step. The improvement of the All+AMR model over the All+LexComp model is also significant7, likely because the semantic features in the AMR graphs are more relevant to paraphrasability than literality.

Footnote 5: \(p=0.000556\) according to McNemar’s Test with continuity correction; the same test is used henceforth

Footnote 6: At \(p=0.000172\)

Footnote 7: \(p=0.00169\) on binary classification and \(p=0.000667\) on three-way classification

Table 5 shows the paraphrasability labeled predicted by the All+AMR model. While it was able to identify most of the Noun inputs, it did so for only half of the Adverb ones. The most challenging turned out to be the Nil inputs, which the model succeeded in detecting less than one-third of the time.

### Paraphrase generation

Despite its higher accuracy in paraphrasability prediction, the Majority baseline (0.264 paraphrase accuracy) performed worse than the Word Frequency baseline (0.275) in paraphrase generation. This likely reflects the greater challenge in identifying the correct Noun type paraphrases than the Advice and Nil types.

For the other models, performance in para 

phrasability prediction is largely correlated to paraphrase generation. The All model improved over the Majority baseline both in terms of word order (0.416) and paraphrase accuracy (0.318). Consider the input "... the apocalyptic destruction of the town and the cult". The All model correctly declined to paraphrase (_null_ output) on the basis of the high score secured by the light-verb paraphrase "the town and the cult suffer an apocalyptic destruction". In contrast, the Majority baseline produced the inappropriate paraphrase "the apocalypes destroy the town and the cult".

The All+AMR model again offered the best performance, at 0.462 word order accuracy and 0.347 paraphrase accuracy.8 For the sentence in Figure 1, the All model generated the predicative adjective paraphrase "interpretation of law is constitutional" due to the high LM score, even though the word "constitutional" yields a different meaning in this paraphrase. The All+AMR model was able to reject this paraphrase since the word "constitution" was inferred to play the subject role. Conversely, in Figure 2, the model was able to reject paraphrases involving the noun "secularism", since the AMR parser annotated with the original adjective "secular". The considerable performance gap from the Gold model (paraphrase accuracy 0.591), however, indicates there is still much room for improvement in interpreting nominalizations.

Footnote 8: The improvement in word order accuracy is statistically significant over the All and All+LexComp models at \(p=0.0211\) and \(p=0.0482\), respectively. The improvement in paraphrase accuracy is not significant, however, at \(p=0.0970\) and \(p=0.428\) against the All and All+LexComp models, respectively.

## 9 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 paraphrascability 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.

#### Acknowledgments

This project was supported by an HKSAR UGC Teaching Learning Grant (Meeting the Challenge of Teaching and Learning Language in the University: Enhancing Linguistic Competence and Performance in English and Chinese, 2016-19 Triennium).

\begin{table}
\begin{tabular}{l|c|c|c|c|c|c|c} \hline Metric \(\rightarrow\) & \multicolumn{4}{c|}{Paraphrasability Prediction} & Paraphrase Generation \\ \cline{2-7}  & \multicolumn{4}{c|}{Noun vs. non-Noun} & 3-way & Word order & Paraphrase \\ \cline{2-7} \(\downarrow\) Model & P & R & F\({}_{1}\) & Accuracy & Accuracy & Accuracy & Accuracy \\ \hline Word Frequency & 0.686 & 0.911 & 0.783 & 0.660 & 0.645 & 0.384 & 0.275 \\ Majority & 0.673 & **1.000** & 0.804 & 0.673 & 0.673 & 0.376 & 0.264 \\ All & 0.743 & 0.873 & 0.802 & 0.711 & 0.671 & 0.416 & 0.318 \\ All+LexComp & 0.752 & 0.859 & 0.802 & 0.715 & 0.675 & 0.424 & 0.333 \\ All+AMR & **0.784** & 0.932 & **0.852** & **0.782** & **0.744** & **0.462** & **0.347** \\ \hline Gold & 1 & 1 & 1 & 1 & 1 & 0.704 & 0.591 \\ \hline \end{tabular}
\end{table}
Table 4: Performance on paraphrasability prediction and paraphrase generation

\begin{table}
\begin{tabular}{l|c c c} \hline Predicted \(\rightarrow\) & Noun & Adverb & Nil \\ \(\downarrow\) Gold & & & \\ \hline Noun & **345** & 13 & 12 \\ Adverb & 17 & **24** & 8 \\ Nil & **78** & 13 & 40 \\ \hline \end{tabular}
\end{table}
Table 5: Contingency table for the paraphrasability prediction of the All+AMR model 



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