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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 – 8 of 8 1 Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Gheini, Mozhdeh; May, Jonathan. - : Underline Science Inc., 2021 BASE Show details 2 RICA: Evaluating Robust Inference Capabilities Based on Commonsense Axioms ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ho, Daniel; Khanna, Rahul; Lee, Seyeon; Lin, Yuchen; Pujara, Jay; Ren, Xiang; Zhou, Pei. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.598/ Abstract: Pre-trained language models (PTLMs) have achieved impressive performance on commonsense inference benchmarks, but their ability to employ commonsense to make robust inferences, which is crucial for effective communications with humans, is debated. In the pursuit of advancing fluid human-AI communication, we propose a new challenge, RICA: Robust Inference using Commonsense Axioms, that evaluates robust commonsense inference despite textual perturbations. To generate data for this challenge, we develop a systematic and scalable procedure using commonsense knowledge bases and probe PTLMs across two different evaluation settings. Extensive experiments on our generated probe sets with more than 10k statements show that PTLMs perform no better than random guessing on the zero-shot setting, are heavily impacted by statistical biases, and are not robust to perturbation attacks. We also find that fine-tuning on similar statements offer ... Keyword: Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing URL: https://dx.doi.org/10.48448/ft0n-sd87 https://underline.io/lecture/37927-rica-evaluating-robust-inference-capabilities-based-on-commonsense-axioms BASE Hide details 3 Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Jin, Xisen; Lin, Yuchen. - : Underline Science Inc., 2021 BASE Show details 4 RockNER: A Simple Method to Create Adversarial Examples for Evaluating the Robustness of Named Entity Recognition Models ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Gao, Wenyang; Lin, Yuchen. - : Underline Science Inc., 2021 BASE Show details 5 ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Han, Rujun; Peng, Nanyun. - : Underline Science Inc., 2021 BASE Show details 6 Discretized Integrated Gradients for Explaining Language Models ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ren, Xiang; Sanyal, Soumya. - : Underline Science Inc., 2021 BASE Show details 7 Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Galstyan, Aram; Mehrabi, Ninareh. - : Underline Science Inc., 2021 BASE Show details 8 Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Han, Jiawei; Mao, Yuning. - : Underline Science Inc., 2021 BASE Show details

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