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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 Hits 1 – 20 of 65 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 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 4 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 5 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 6 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 7 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 8 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 9 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 10 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 11 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 12 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 13 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 14 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 15 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 16 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 17 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 18 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. - : Underline Science Inc., 2021 BASE Show details 19 EM ALBERT: a step towards equipping Manipuri for NLP ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Huidrom, Rudali; Lepage, Yves. - : Underline Science Inc., 2021 BASE Show details 20 ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021. - : Underline Science Inc., 2021 Abstract: The ability to detect Out-of-Domain (OOD) inputs has been a critical requirement in many real-world NLP applications. For example, intent classification in dialogue systems. The reason is that the inclusion of unsupported OOD inputs may lead to catastrophic failure of systems. However, it remains an empirical question whether current methods can tackle such problems reliably in a realistic scenario where zero OOD training data is available. In this study, we propose ProtoInfoMax, a new architecture that extends Prototypical Networks to simultaneously process in-domain and OOD sentences via Mutual Information Maximization (InfoMax) objective. Experimental results show that our proposed method can substantially improve performance up to 20% for OOD detection in low resource settings of text classification. We also show that ProtoInfoMax is less prone to typical overconfidence errors of Neural Networks, leading to more reliable prediction results. ... Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Neural Network URL: https://dx.doi.org/10.48448/f0yb-qc13 https://underline.io/lecture/40607-protoinfomax-prototypical-networks-with-mutual-information-maximization-for-out-of-domain-detection BASE Hide details Page: 1 2 3 4

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