Divide and Conquer: Crowdsourcing the Creation of Cross-Lingual Textual Entailment Corpora

By Matteo Negri, Luisa Bentivogli, Yashar Mehdad, Danilo Giampiccolo, Alessandro Marchetti. 2011

91 citations recorded by the source corpus.

Subfields: Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

XNLI: Evaluating Cross-lingual Sentence Representations (2018, background): “There have also been efforts to build multilingual RTE datasets, either through translating English data (Mehdad et al., 2011), or annotating sentences from a parallel corpora (Negri et al., 2011).”
Corpus Annotation through Crowdsourcing: Towards Best Practice Guidelines (2014, background): “, 2010), word sense disambiguation (Parent and Eskenazi, 2010), textual entailment (Negri et al., 2011), question answering (Heilman and Smith, 2010).”
Design and realization of a modular architecture for textual entailment (2013, background): “More recently, there has been interest in cross-lingual textual entailment (Mehdad, Negri, and Federico, 2010; Mehdad, Negri, and Federico, 2011; Negri et al., 2011).”
OCNLI: Original Chinese Natural Language Inference (2020, background): “Efforts have largely focused on automatic translation of existing English resources (Mehdad et al., 2011), sometimes coupled with smaller-scale hand annotation by native speakers (Negri et al., 2011; Agi´c and Schluter, 2017).”
Detecting hallucinations in large language models using semantic entropy (2024, unknown): “We operationalize E ( ⋅ , ⋅ ) using the idea of bidirectional entailment, which has a long history in linguistics 52 and natural language processing 28,53,54 .”
Crowdsourcing Inference-Rule Evaluation (2012, methodology): “Deﬁning “entailment” is quite difﬁcult when dealing with expert annotators and still more with non-experts, as was noted by Negri et al. (2011).”
Baselines and Test Data for Cross-Lingual Inference (2017, background): “to SNLI, there has been work in cross-lingual textual entailment using parallel corpora (Mehdad et al., 2011) and lexical resources (Castillo, 2011), or crowdsourcing for multilingual training data byNegri et al. (2011). We also note two shared tasks, on cross-lingual entailment with ﬁve languages (”
Exploiting Qualitative Information from Automatic Word Alignment for Cross-lingual NLP Tasks (2013, methodology): “Each dataset was created with the crowdsourcing-based method described in Negri et al. (2011), and consists of 1000 T 1 –”