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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 62 1 Natural Language Generation : From Data Creation to Evaluation via Modelling ; Génération en langue naturelle : de la création des données à l'évaluation, en passant par la modélisation Shimorina, Anastasia. - : HAL CCSD, 2021 In: https://hal.univ-lorraine.fr/tel-03254708 ; Computation and Language [cs.CL]. Université de Lorraine, 2021. English. ⟨NNT : 2021LORR0080⟩ (2021) BASE Show details 2 TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Iryna; Reimers, Nils. - : Underline Science Inc., 2021 BASE Show details 3 Truth-Conditional Captions for Time Series Data ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Berg-Kirkpatrick, Taylor; Jhamtani, Harsh. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.55/ Abstract: In this paper, we explore the task of automatically generating natural language descriptions of salient patterns in a time series, such as the stock prices of a company over a week. A model for this task should be able to extract high-level patterns such as the presence of a peak or a dip. While typical contemporary neural models with attention mechanisms can generate fluent output descriptions for this task, they often generate factually incorrect descriptions. We propose a computational model with a truth-conditional architecture which first runs small learned programs on the input time series, then identifies the programs/patterns which hold true for the given input, and finally conditions on \emph{only} the chosen valid program (rather than the input time series) to generate the output text description. A program in our model is constructed from modules, which are small neural networks that are designed to capture numerical ... Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural language generation; Natural Language Processing URL: https://dx.doi.org/10.48448/zpvw-qs56 https://underline.io/lecture/37906-truth-conditional-captions-for-time-series-data BASE Hide details 4 CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Piyush; Akula, Arjun. - : Underline Science Inc., 2021 BASE Show details 5 IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Sebastian; Bahar, Syafri. - : 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 Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Dai, Bo; Jiang, Haoming. - : Underline Science Inc., 2021 BASE Show details 8 A Comprehensive Comparison of Word Embeddings in Event & Entity Coreference Resolution. ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ittoo, Ashwin; POUMAY, Judicael. - : Underline Science Inc., 2021 BASE Show details 9 Benchmarking Meta-embeddings: What Works and What Does Not ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., German; Agerri, Rodrigo. - : Underline Science Inc., 2021 BASE Show details 10 The statistical advantage of automatic NLG metrics at the system level ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Jia, Robin; Wei, Johnny. - : Underline Science Inc., 2021 BASE Show details 11 QA-Align: Representing Cross-Text Content Overlap by Aligning Question-Answer Propositions ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Brook Weiss, Daniela. - : Underline Science Inc., 2021 BASE Show details 12 Paraphrase Generation: A Survey of the State of the Art ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Bhat, Suma; Zhou, Jianing. - : Underline Science Inc., 2021 BASE Show details 13 Measuring Association Between Labels and Free-Text Rationales ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Marasovic, Ana; Smith, Noah A.. - : Underline Science Inc., 2021 BASE Show details 14 Coupling Context Modeling with Zero Pronoun Recovering for Document-Level Natural Language Generation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Tan, Xin; Zhang, Longyin. - : Underline Science Inc., 2021 BASE Show details 15 Refocusing on Relevance: Personalization in NLG ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Bedrick, Steven; Dudy, Shiran. - : Underline Science Inc., 2021 BASE Show details 16 Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Deng, Mingkai; Hu, Zhiting. - : Underline Science Inc., 2021 BASE Show details 17 VisualSem: a high-quality knowledge graph for vision and language ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Liu, Yibo. - : Underline Science Inc., 2021 BASE Show details 18 Entity-Based Semantic Adequacy for Data-to-Text Generation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Faille, Juliette; Gardent, Claire. - : Underline Science Inc., 2021 BASE Show details 19 Parallel Refinements for Lexically Constrained Text Generation with BART ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; He, Xingwei. - : Underline Science Inc., 2021 BASE Show details 20 A practical perspective on connective generation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Vera; Scholman, Merel. - : Underline Science Inc., 2021 BASE Show details Page: 1 2 3 4

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