@inproceedings{amanaki-etal-2022-fine,
title = "Fine-grained Entailment: Resources for {G}reek {NLI} and Precise Entailment",
author = "Amanaki, Eirini and
Bernardy, Jean-Philippe and
Chatzikyriakidis, Stergios and
Cooper, Robin and
Dobnik, Simon and
Karimi, Aram and
Ek, Adam and
Giannikouri, Eirini Chrysovalantou and
Katsouli, Vasiliki and
Kolokousis, Ilias and
Mamatzaki, Eirini Chrysovalantou and
Papadakis, Dimitrios and
Petrova, Olga and
Psaltaki, Erofili and
Soupiona, Charikleia and
Skoulataki, Effrosyni and
Stefanidou, Christina",
editor = {S{\"a}lev{\"a}, Jonne and
Lignos, Constantine},
booktitle = "Proceedings of the Workshop on Dataset Creation for Lower-Resourced Languages within the 13th Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.dclrl-1.6",
pages = "44--52",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Fine-grained Entailment: Resources for Greek NLI and Precise Entailment
%A Amanaki, Eirini
%A Bernardy, Jean-Philippe
%A Chatzikyriakidis, Stergios
%A Cooper, Robin
%A Dobnik, Simon
%A Karimi, Aram
%A Ek, Adam
%A Giannikouri, Eirini Chrysovalantou
%A Katsouli, Vasiliki
%A Kolokousis, Ilias
%A Mamatzaki, Eirini Chrysovalantou
%A Papadakis, Dimitrios
%A Petrova, Olga
%A Psaltaki, Erofili
%A Soupiona, Charikleia
%A Skoulataki, Effrosyni
%A Stefanidou, Christina
%Y Sälevä, Jonne
%Y Lignos, Constantine
%S Proceedings of the Workshop on Dataset Creation for Lower-Resourced Languages within the 13th Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F amanaki-etal-2022-fine
%X 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.
%U https://aclanthology.org/2022.dclrl-1.6
%P 44-52
Markdown (Informal)
[Fine-grained Entailment: Resources for Greek NLI and Precise Entailment](https://aclanthology.org/2022.dclrl-1.6) (Amanaki et al., DCLRL 2022)
ACL
- 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, and Christina Stefanidou. 2022. Fine-grained Entailment: Resources for Greek NLI and Precise Entailment. In Proceedings of the Workshop on Dataset Creation for Lower-Resourced Languages within the 13th Language Resources and Evaluation Conference, pages 44–52, Marseille, France. European Language Resources Association.