{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fine-grained-entailment-resources-for-greek","title":"Fine-grained Entailment: Resources for Greek NLI and Precise Entailment","arxiv_id":null,"date":"2022-06-01","proceeding":"DCLRL (LREC) 2022 6","authors":["Eirini Amanaki","Jean-Philippe Bernardy","Stergios Chatzikyriakidis","Robin Cooper","Simon Dobnik","Aram Karimi","Adam Ek","Eirini Chrysovalantou Giannikouri","Vasiliki Katsouli","Ilias Kolokousis","Eirini Chrysovalantou Mamatzaki","Dimitrios Papadakis","Olga Petrova","Erofili Psaltaki","Charikleia Soupiona","Effrosyni Skoulataki","Christina Stefanidou"],"abstract":"In this paper, we present a number of fine-grained resources for Natural Language Inference (NLI). In particular, we present a number of resources and validation methods for Greek NLI and a resource for precise NLI. First, we extend the Greek version of the FraCaS test suite to include examples where the inference is directly linked to the syntactic/morphological properties of Greek. The new resource contains an additional 428 examples, making it in total a dataset of 774 examples. Expert annotators have been used in order to create the additional resource, while extensive validation of the original Greek version of the FraCaS by non-expert and expert subjects is performed. Next, we continue the work initiated by (CITATION), according to which a subset of the RTE problems have been labeled for missing hypotheses and we present a dataset an order of magnitude larger, annotating the whole SuperGlUE/RTE dataset with missing hypotheses. Lastly, we provide a de-dropped version of the Greek XNLI dataset, where the pronouns that are missing due to the pro-drop nature of the language are inserted. We then run some models to see the effect of that insertion and report the results.","url_abs":"https://aclanthology.org/2022.dclrl-1.6","url_pdf":"https://aclanthology.org/2022.dclrl-1.6.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fine-grained-entailment-resources-for-greek","repo_url":"https://github.com/gu-clasp/lrec_2022","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"natural-language-inference","task_name":"Natural Language Inference"},{"task_slug":"rte","task_name":"RTE"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}