Paraphrasentypen

Paraphrases are texts that convey the same meaning using different words or structures. While current paraphrase generation and detection techniques are good at producing and identifying semantically similar content [1], they fail to understand the linguistic characteristics that make two texts alike. Thus, paraphrase generation and detection (PGD) tasks and approaches are conditioned on limited features. For example, the most used task in paraphrase detection still restricts itself to identifying if a sentence pair shares the same meaning using a binary zero or one prediction. Paraphrase types [2], which represent specific linguistic forms of paraphrases (e.g., syntax), offer the necessary information to understand what changes make paraphrased and original content similar. We use two tasks to explore these types, paraphrase type generation and detection. While the former generates paraphrases according to specific linguistic changes (e.g., lexicon), the latter must detect them (Figure 1). Current PGD approaches do not consider such types in their architectures or training objectives and often fail at various stages [2].
A detailed perspective on what composes paraphrased content helps us understand the relationship between text segments from various sources (e.g., human and machine). For example, the granular understanding of the linguistic changes involved in paraphrase generation could be directly applied to support language learners. A system could provide simpler paraphrases considering specific linguistic variations (e.g., syntax) to support students in learning new words and concepts. In the case of paraphrase detection, the evaluation of paraphrase types helps identify which structures characterize human and machine authors, thus providing accurate support in possible plagiarism cases. Recent work by Meta [3] on pre-training shows the limitation of language models of individual word choice originating from one-word-at-a-time training and argues that individual use of words is negligible compared to the semantic concepts a model learns. Semantic identity or contradiction by individual word choice is exactly what paraphrase types represent. Hence, paraphrase types have the potential to transform current PGD solutions, enable various downstream tasks, and improve the training of language models. Despite the impact of LLMs (e.g., ChatGPT2 and Gemini3) in our daily lives, there are no marked efforts to understand how these models use linguistic characteristics to produce content and to infer if text segments are semantically equivalent. Most work either applies techniques to refine models’ reasoning abilities (e.g., Chain-of-Thought [4]) or propose architectures to improve their detection scores [1], [5] for standard tasks and datasets (i.e., without paraphrase types). Yet, the potential of paraphrase types remains largely unexplored. Paraphrase types can transform how we solve PGD tasks and co-related problems (e.g., plagiarism detection, recommender systems). Therefore, this project's main objective is to “Design, implement, and evaluate an approach to learn paraphrase types for paraphrase generation and detection.”