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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 – 7 of 7 1 Value-aware Approximate Attention ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Berant, Jonathan; Gupta, Ankit. - : Underline Science Inc., 2021 BASE Show details 2 Memory-efficient Transformers via Top-k Attention ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Shaya; Berant, Jonathan. - : Underline Science Inc., 2021 BASE Show details 3 Achieving Model Robustness through Discrete Adversarial Training ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Berant, Jonathan; Ivgi, Maor. - : Underline Science Inc., 2021 BASE Show details 4 COVR: A Test-Bed for Visually Grounded Compositional Generalization with Real Images ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Berant, Jonathan; Bogin, Ben; Gardner, Matt; Gupta, Shivanshu. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.774/ Abstract: While interest in models that generalize at test time to new compositions has risen in recent years, benchmarks in the visually-grounded domain have thus far been restricted to synthetic images. In this work, we propose COVR, a new test-bed for visually-grounded compositional generalization with real images. To create COVR, we use real images annotated with scene graphs, and propose an almost fully automatic procedure for generating question-answer pairs along with a set of context images. COVR focuses on questions that require complex reasoning, including higher-order operations such as quantification and aggregation. Due to the automatic generation process, COVR facilitates the creation of compositional splits, where models at test time need to generalize to new concepts and compositions in a zero- or few-shot setting. We construct compositional splits using COVR and demonstrate a myriad of cases where state-of-the-art ... Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Text Generation URL: https://dx.doi.org/10.48448/ccz0-bc51 https://underline.io/lecture/37494-covr-a-test-bed-for-visually-grounded-compositional-generalization-with-real-images BASE Hide details 5 Transformer Feed-Forward Layers Are Key-Value Memories ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Berant, Jonathan; Geva, Mor. - : Underline Science Inc., 2021 BASE Show details 6 What's in Your Head? Emergent Behaviour in Multi-Task Transformer Models ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ben-Arie, Aviv; Berant, Jonathan. - : Underline Science Inc., 2021 BASE Show details 7 Finding needles in a haystack: Sampling Structurally-diverse Training Sets from Synthetic Data for Compositional Generalization ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Berant, Jonathan; Herzig, Jonathan. - : Underline Science Inc., 2021 BASE Show details

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