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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 18 of 18 1 Automatic Fact-Checking with Document-level Annotations using BERT and Multiple Instance Learning ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Park, Joonsuk; Sathe, Aalok. - : Underline Science Inc., 2021 BASE Show details 2 FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Bansal, Mohit; Guo, Han. - : Underline Science Inc., 2021 BASE Show details 3 Open Aspect Target Sentiment Classification with Natural Language Prompts ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Birle, Ian; Chang, Haw-Shiuan. - : Underline Science Inc., 2021 BASE Show details 4 Does External Knowledge Help Explainable Natural Language Inference? Automatic Evaluation vs. Human Ratings ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Schuff, Hendrik. - : Underline Science Inc., 2021 Abstract: Natural language inference (NLI) requires models to learn and apply commonsense knowledge. These reasoning abilities are particularly important for explainable NLI systems that generate a natural language explanation in addition to their label prediction. The integration of external knowledge has been shown to improve NLI systems, here we investigate whether it can also improve their explanation capabilities. For this, we investigate different sources of external knowledge and evaluate the performance of our models on in-domain data as well as on special transfer datasets that are designed to assess fine-grained reasoning capabilities. We find that different sources of knowledge have a different effect on reasoning abilities, for example, implicit knowledge stored in language models can hinder reasoning on numbers and negations. Finally, we conduct the largest and most fine-grained explainable NLI crowdsourcing study to date. It reveals that even large differences in automatic performance scores do neither ... Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Inference; Natural Language Processing; Neural Network URL: https://underline.io/lecture/39896-does-external-knowledge-help-explainable-natural-language-inferencequestion-automatic-evaluation-vs.-human-ratings https://dx.doi.org/10.48448/wbph-r951 BASE Hide details 5 ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; D. Manning, Christopher; Koreeda, Yuta. - : Underline Science Inc., 2021 BASE Show details 6 IndoNLI: A Natural Language Inference Dataset for Indonesian ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Aji, Alham Fikri; Louvan, Samuel. - : Underline Science Inc., 2021 BASE Show details 7 Investigating the Effect of Natural Language Explanations on Out-of-Distribution Generalization in Few-shot NLI ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Tan, Chenhao; Zhou, Yangqiaoyu. - : Underline Science Inc., 2021 BASE Show details 8 Don't Discard All the Biased Instances: Investigating a Core Assumption in Dataset Bias Mitigation Techniques ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Amirkhani, Hossein; Pilehvar, Mohammad Taher. - : Underline Science Inc., 2021 BASE Show details 9 Continual Few-Shot Learning for Text Classification ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Ramakanth; Bansal, Mohit. - : Underline Science Inc., 2021 BASE Show details 10 Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021. - : Underline Science Inc., 2021 BASE Show details 11 Scheduled Sampling Based on Decoding Steps for Neural Machine Translation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Liu, Yijin. - : Underline Science Inc., 2021 BASE Show details 12 Pairwise Supervised Contrastive Learning of Sentence Representations ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Zhang, Dejiao. - : Underline Science Inc., 2021 BASE Show details 13 Finding a Balanced Degree of Automation for Summary Evaluation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Bansal, Mohit; Zhang, Shiyue. - : Underline Science Inc., 2021 BASE Show details 14 A Multilingual Benchmark for Probing Negation-Awareness with Minimal Pairs ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; de Lhoneux, Miryam; Hartmann, Mareike. - : Underline Science Inc., 2021 BASE Show details 15 Universal Sentence Representation Learning with Conditional Masked Language Model ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Cer, Daniel; Darve, Eric. - : Underline Science Inc., 2021 BASE Show details 16 BARThez: a Skilled Pretrained French Sequence-to-Sequence Model ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Kamal Eddine, Moussa; Tixier, Antoine. - : Underline Science Inc., 2021 BASE Show details 17 Nearest Neighbour Few-Shot Learning for Cross-lingual Classification ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Bari, M Saiful; Haidar, Batool. - : Underline Science Inc., 2021 BASE Show details 18 Subword Mapping and Anchoring across Languages ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021. - : Underline Science Inc., 2021 BASE Show details

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