| Challenge: | Existing studies on Tabular Natural Language Inference (TNLI) focus on monolingual settings where tabular premise and hypothesis are in the same language. |
| Approach: | They propose a task where tabular premise and hypothesis are in two languages . they translate textual hypotheses from an English-indic TNLI dataset into eleven major languages - english and indic . |
| Outcome: | The proposed model performs well on a bilingual dataset in English and in 11 major Indian languages. |
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| Challenge: | Indic NLP has made rapid advances in terms of corpora and pre-trained models, but benchmark datasets on standard NLU tasks are limited. |
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OCNLI: Original Chinese Natural Language Inference (2020.findings-emnlp)
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| Challenge: | Recent efforts to extend natural language understanding to other languages have focused on (automatically) translating existing English datasets. |
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Baselines and Test Data for Cross-Lingual Inference (L18-1)
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| Challenge: | Recent research on textual entailment is limited to English, but it is expanding to other languages. |
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When Your Cousin Has the Right Connections: Unsupervised Bilingual Lexicon Induction for Related Data-Imbalanced Languages (2024.lrec-main)
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A Deep Transfer Learning Method for Cross-Lingual Natural Language Inference (2022.lrec-1)
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| Challenge: | Natural Language Inference (NLI) is a crucial task in AI and natural language processing. |
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| Challenge: | Existing studies focus on sentence-level inference, which limits its application in downstream NLP problems. |
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IMPLI: Investigating NLI Models’ Performance on Figurative Language (2022.acl-long)
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| Challenge: | Understanding figurative language is a difficult area in NLP but is essential for proper understanding. |
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| Challenge: | Existing models fail to capture important semantic features of logic such as monotonicity and negation. |
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| Challenge: | Existing datasets for natural language inference (NLI) are limited to English and a few other well-resourced languages. |
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XplaiNLI: Explainable Natural Language Inference through Visual Analytics (2020.coling-demos)
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| Challenge: | Recent research has revealed some heuristics and biases of natural language inference models. |
| Approach: | They propose an interactive visualization interface that computes Natural Language Inference with different methods and provides explanations for the decisions made by each approach. |
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