CompArg: Comparative Sentences 2019 dataset for comparative argument mining is composed of sentences annotated with BETTER / WORSE markers (the first object is better / worse than the second object) or NONE (the sentence does not contain a comparison of the target objects). The BETTER sentences stand for a pro-argument in favor of the first compared object and WORSE-sentences represent a con-argument and favor the second object.

Sample sentences from CompSent-19 dataset [9], with preference indications. Note: Sequence matters—preferences reference the initial entity in comparison to the subsequent one.

19 page typed manuscript. -- Draft prepared by Capell. Part IV Malekula Comparative Grammar. Foreward and sound laws of Malekula.; Date of recording unknown.. Language as given: Malekula

1 page manuscript. -- Draft by Capell. The Comparative Grammar notes. Languages are Efate, Pangkumu, Malo, Baki, Bieri, Tanna and Efate.; Date of recording unknown.. Language as given: Efate, Pangkumu (Rerep), Malo, Baki, Bieri (Bieria), Tanna, Futuna

The extraction of subjective comparative relations is essential in the field of question answering systems, playing a crucial role in accurately interpreting and addressing complex questions. To tackle this challenge, we propose the SCQRE model, specifically designed to extract subjective comparative relations from questions by focusing on entities, aspects, constraints, and preferences. Our approach leverages multi-task learning, the Natural Language Inference (NLI) paradigm, and a specialized adapter integrated into RoBERTa_base_go_emotions to enhance performance in Element Extraction (EE), Compared Elements Identification (CEI), and Comparative Preference Classification (CPC). Key innovations include handling X- and XOR-type preferences, capturing implicit comparative nuances, and the robust extraction of constraints often neglected in existing models. We also introduce the Smartphone-SCQRE dataset, along with another domain-specific dataset, Brands-CompSent-19-SCQRE, both structured as subjective comparative questions. Experimental results demonstrate that our model outperforms existing approaches across multiple question-level and sentence-level datasets and surpasses recent language models, such as GPT-3.5-turbo-0613, Llama-2-70b-chat, and Qwen-1.5-7B-Chat, showcasing its effectiveness in question-based comparative relation extraction.

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