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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 Page: 1 2 3 4 5...29 Hits 1 – 20 of 572 1 Ara-Women-Hate: The first Arabic Hate Speech corpus regarding Women ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Guellil, Imane. - : Underline Science Inc., 2021 BASE Show details 2 Towards the Early Detection of Child Predators in Chat Rooms: A BERT-based Approach ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Kumbale, Sinchana; Singh, Smriti. - : Underline Science Inc., 2021 BASE Show details 3 One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasets ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Damonte, Marco; Monti, Emilio. - : Underline Science Inc., 2021 BASE Show details 4 STaCK: Sentence Ordering with Temporal Commonsense Knowledge ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ghosal, Deepanway; Majumder, Navonil. - : Underline Science Inc., 2021 BASE Show details 5 Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Chang, Kai-Wei; Hsieh, Cho-Jui. - : Underline Science Inc., 2021 BASE Show details 6 Graphine: A Dataset for Graph-aware Terminology Definition Generation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Gu, Yiyang; Liu, Zequn. - : Underline Science Inc., 2021 BASE Show details 7 End-to-end style-conditioned poetry generation: What does it take to learn from examples alone? ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Eger, Steffen. - : Underline Science Inc., 2021 BASE Show details 8 To what extent do human explanations of model behavior align with actual model behavior? ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Prasad, Grusha. - : Underline Science Inc., 2021 BASE Show details 9 Time-aware Graph Neural Network for Entity Alignment between Temporal Knowledge Graphs ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Lehmann, Jens; Su, Fenglong. - : Underline Science Inc., 2021 BASE Show details 10 What’s Hidden in a One-layer Randomly Weighted Transformer? ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Shen, Sheng; Yao, Zhewei. - : Underline Science Inc., 2021 BASE Show details 11 Finetuning Pretrained Transformers into RNNs ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Chen, Weizhu; Ilharco, Gabriel. - : Underline Science Inc., 2021 BASE Show details 12 Comparing Span Extraction Methods for Semantic Role Labeling ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Zhang, Zhisong. - : Underline Science Inc., 2021 BASE Show details 13 Sometimes We Want Ungrammatical Translations ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Parthasarathi, Prasanna. - : Underline Science Inc., 2021 BASE Show details 14 Pruning Neural Machine Translation for Speed Using Group Lasso ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Behnke, Maximiliana; Heafield, Kenneth. - : Underline Science Inc., 2021 BASE Show details 15 Elementary-Level Math Word Problem Generation using Pre-Trained Transformers ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Savindu; Athapaththu, Dineth. - : Underline Science Inc., 2021 BASE Show details 16 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 BASE Show details 17 The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ahia, Orevaoghene; Hooker, Sara. - : Underline Science Inc., 2021 BASE Show details 18 Knowledge Graph Representation Learning using Ordinary Differential Equations ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Hoffmann, Franca; Lehmann, Jens. - : Underline Science Inc., 2021 BASE Show details 19 What Models Know About Their Attackers: Deriving Attacker Information From Latent Representations ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Adam; Asthana, Kalyani. - : Underline Science Inc., 2021 BASE Show details 20 Mind the Context: The Impact of Contextualization in Neural Module Networks for Grounding Visual Referring Expressions ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Akula, Arjun; Gella, Spandana; Reddy, Siva; Wang, Keze; Zhu, Song-Chun. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.516/ Abstract: Neural module networks (NMN) are a popular approach for grounding visual referring expressions. Prior implementations of NMN use pre-defined and fixed textual inputs in their module instantiation. This necessitates a large number of modules as they lack the ability to share weights and exploit associations between similar textual contexts (e.g. 'dark cube on the left' vs. 'black cube on the left'). In this work, we address these limitations and evaluate the impact of contextual clues in improving the performance of NMN models. First, we address the problem of fixed textual inputs by parameterizing the module arguments. This substantially reduce the number of modules in NMN by up to 75% without any loss in performance. Next we propose a method to contextualize our parameterized model to enhance the module’s capacity in exploiting the visiolinguistic associations. Our model outperforms the state-of-the-art NMN model on CLEVR-Ref+ ... Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Neural Network URL: https://dx.doi.org/10.48448/c8vt-s207 https://underline.io/lecture/37933-mind-the-context-the-impact-of-contextualization-in-neural-module-networks-for-grounding-visual-referring-expressions BASE Hide details Page: 1 2 3 4 5...29

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