Paraphrase

History

Although paraphrases likely abounded in oral traditions, paraphrasing as a specific educational exercise dates back to at least Roman times, when the author Quintilian recommended it for students to develop dexterity in language. In the Middle Ages, this tradition continued, with authors such as Geoffrey of Vinsauf developing schoolroom exercises that included both rhetorical manipulations and paraphrasing as a way of generating poems and speeches. There is interest in the study of paraphrases relating to concerns around plagiarism and original authorship.

Types

For the purposes of education, Fred Inglis identifies five levels of paraphrase:

1. replacing words with synonyms
2. varying sentence structure
3. reordering information
4. turning long sentences into multiple shorter ones (or vice versa)
5. expressing abstract concepts more concretely.

Paraphrasing with synonyms is considered by some to be an acceptable stage in teaching paraphrase, but it is necessary that it is ultimately combined with techniques for altering sentence structure to avoid the appearance of plagiarism. Studies of English language students have found that ESL learners tend to rely on using synonyms rather than changing sentence structure when paraphrasing. Participants in a study of some Vietnamese ESL learners expressed that they preferred using synonyms out of a fear that using the wrong sentence structure would lead to the sentence having a different meaning. Na and Mai suggest that ESL teachers should provide varied activities including tasks requiring changes in syntax, and that ESL students should be given source texts to paraphrase whose meaning they are already readily able to understand.

Natural Language Processing researchers have defined various (atomic) paraphrase types to better understand how paraphrasing occurs in humans. These types fall into six broad categories, each reflecting different ways in which a text can be altered to convey the same or similar meaning:

1. morphology-based changes
2. lexicon-based changes
3. lexico-syntactic based changes
4. syntax-based changes
5. discourse-based changes
6. extremes.

Morphology-based changes involve alterations at the level of word formation, such as changing the tense of verbs or the number of nouns. For instance, converting "walks" to "walked" represents a morphological change by altering the verb's tense. Lexicon-based changes include changes made by substituting words with their synonyms or closely related words without significantly altering the sentence structure. An example could be changing "quick" to "fast" in a sentence, where both adjectives convey a similar speed attribute. Lexico-syntactic-based changes contain both lexical alterations and modifications in the sentence structure. An example might be transforming an active voice sentence like "The cat chased the mouse" into a passive voice "The mouse was chased by the cat," where both the sentence structure and some words are altered. Syntax-based changes are primarily focused on the structure of the sentence rather than the words themselves. For example, changing a complex sentence into two simpler sentences while maintaining the overall meaning falls into this category. Discourse-based changes are alterations that affect the larger discourse or text structure, such as reordering points in a paragraph or changing the way arguments are presented without altering the factual content. Extremes include changes that significantly alter the text, possibly introducing new information or omitting crucial details, thus pushing the boundaries of what might typically be considered a paraphrase.

Machine learning models have been trained to generate paraphrases with specific properties, such as high semantic similarity and syntactic diversity, or to generate specific paraphrase types. Models that have high capacity in paraphrasing can be used for various applications. For example, the granular understanding of the linguistic changes involved in paraphrase generation could be directly applied to support language learners. A model can provide simpler paraphrases considering specific linguistic variations (e.g., syntax) to support students in learning new words and concepts. Universities could create a linguistic profile of their students based on their assignments and better assess their thesis with content similarity detection for potential plagiarism cases. Different types of paraphrases such as syntax and lexicon changes have also been used for prompt engineering to adjust prompts in specific linguistic aspects to achieve better model outputs.

Analysis

A paraphrase typically explains or clarifies the text that is being paraphrased. For example, "The signal was red" might be paraphrased as "The train was not allowed to pass as the red signal light was illuminated". A paraphrase can be introduced with verbum dicendi—a declaratory expression to signal the transition to the paraphrase. For example, in "The author states 'The signal was red,' that is, the train was not allowed to proceed," the that is signals the paraphrase that follows.

A paraphrase does not need to accompany a direct quotation. The paraphrase typically serves to put the source's statement into perspective or to clarify the context in which it appeared. A paraphrase is typically more detailed than a summary. One should add the source at the end of the sentence: When the light was red, trains could not go (Wikipedia). A paraphrase may attempt to preserve the essential meaning of the material being paraphrased. Thus, the (intentional or otherwise) reinterpretation of a source to infer a meaning that is not explicitly evident in the source itself qualifies as "original research," and not a paraphrase. Unlike a metaphrase, which represents a "formal equivalent" of the source, a paraphrase represents a "dynamic equivalent" thereof. While a metaphrase attempts to translate a text literally, a paraphrase conveys the essential thought expressed in a source text—if necessary, at the expense of literality. For details, see dynamic and formal equivalence.

In your own words

The phrase "in your own words" is frequently used within this context to imply that the writer has rewritten the text in their own writing style – how they would have written it if they had created the idea. Nowadays, there are some models to learn and recognize paraphrase on natural language texts. Sentences can also be automatically paraphrased using text simplification software.

References

References

1. Stewart, Donald. (1971). "Metaphor and Paraphrase". Philosophy & Rhetoric.
2. D'Angelo. (October 1979). "The Art of Paraphrase". College Composition and Communication.
3. (16 September 2013). "Skills for Academic and Career Success". Pearson Higher Education AU.
4. Inglis, Fred. (2008). "Key Concepts in Education". SAGE Publications.
5. Ruiter, Rik. (2005). "Highway to E.S.L.". iUniverse.
6. (2017). "Paraphrasing in Academic Writing: a Case Study of Vietnamese Learners of English". Language Education in Asia.
7. (2018). "ETPC - A Paraphrase Identification Corpus Annotated with Extended Paraphrase Typology and Negation". European Language Resources Association (ELRA).
8. Levin, Beth. (1993). "English Verb Classes and Alternations: A Preliminary Investigation". University of Chicago Press.
9. Milicevic, Jasmina. (2011-11-10). "La paraphrase".
10. (2015-03-01). "Corpus annotation with paraphrase types: new annotation scheme and inter-annotator agreement measures". Language Resources and Evaluation.
11. (2022). "Quality Controlled Paraphrase Generation". Association for Computational Linguistics.
12. (2023). "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing". Association for Computational Linguistics.
13. (2021). "Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection".
14. (2022). "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing". Association for Computational Linguistics.
15. (2019). "Machine Learning Models for Paraphrase Identification and its Applications on Plagiarism Detection".
16. (2023). "The language of prompting: What linguistic properties make a prompt successful?". Association for Computational Linguistics.
17. (2024). "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing". Association for Computational Linguistics.
18. (2024). "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)". Association for Computational Linguistics.
19. Fenceroy, Edna. (2011-11-01). "Minimizing Conflict Through Restorative Conferencing: Changing Lives Through Changing Attitudes". WestBow Press.
20. (2021-09-22). "Communicative English for Nurses , 3rd Edition - E-Book". Elsevier Health Sciences.
21. Ganguly, Amit. (2018-07-11). "English Communication: E-Book". SBPD Publications.
22. "Decide when to Quote, Paraphrase and Summarize - University of Houston-Victoria".
23. (2021-09-22). "Communicative English for Nurses , 3rd Edition - E-Book". Elsevier Health Sciences.
24. (2021-10-26). "QuillBot Premium".
25. "Writing in your own words". The Open University.
26. Figueroa, Alejandro. (2013). "Learning to Rank Effective Paraphrases from Query Logs for Community Question Answering".
27. Shardlow, Matthew. "[https://web.archive.org/web/20180302163823/https://pdfs.semanticscholar.org/1f50/efc8e572397e58a7b3a2021da737225fecc5.pdf A survey of automated text simplification]." International Journal of Advanced Computer Science and Applications 4.1 (2014): 58–70.