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Meta Distant Transfer Learning for Pre-trained Language Models ...
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Improving Span Representation for Domain-adapted Coreference Resolution ...
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Temporal Adaptation of BERT and Performance on Downstream Document Classification: Insights from Social Media ...
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85 |
An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing ...
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Looking for Confirmations: An Effective and Human-Like Visual Dialogue Strategy ...
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CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization ...
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Latent Hatred: A Benchmark for Understanding Implicit Hate Speech ...
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STaCK: Sentence Ordering with Temporal Commonsense Knowledge ...
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ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning ...
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Weakly supervised discourse segmentation for multiparty oral conversations ...
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Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution ...
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Progressively Guide to Attend: An Iterative Alignment Framework for Temporal Sentence Grounding ...
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Knowledge Enhanced Fine-Tuning for Better Handling Unseen Entities in Dialogue Generation ...
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STANKER: Stacking Network based on Level-grained Attention-masked BERT for Rumor Detection on Social Media ...
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IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation ...
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The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Sebastian; Bahar, Syafri; Cahyawijaya, Samuel; Fung, Pascale; Kuncoro, Adhiguna; Leylia Khodra, Masayu; Li, Xiaohong; Lim, Zhi Yuan; Purwarianti, Ayu; Vincentio, Karissa; Wilie, Bryan; Winata, Genta. - : Underline Science Inc., 2021
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Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.699/ Abstract: Natural language generation (NLG) benchmarks provide an important avenue to measure progress and develop better NLG systems. Unfortunately, the lack of publicly available NLG benchmarks for low-resource languages poses a challenging barrier for building NLG systems that work well for languages with limited amounts of data. Here we introduce IndoNLG, the first benchmark to measure natural language generation (NLG) progress in three low-resource---yet widely spoken---languages of Indonesia: Indonesian, Javanese, and Sundanese. Altogether, these languages are spoken by more than 100 million native speakers, and hence constitute an important use case of NLG systems today. Concretely, IndoNLG covers six tasks: summarization, question answering, chit-chat, and three different pairs of machine translation (MT) tasks. We collate a clean pretraining corpus of Indonesian, Sundanese, and Javanese datasets, Indo4B-Plus, which is used to ...
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Keyword:
Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural language generation; Natural Language Processing
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URL: https://underline.io/lecture/37340-indonlg-benchmark-and-resources-for-evaluating-indonesian-natural-language-generation https://dx.doi.org/10.48448/nbeq-zy04
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SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis ...
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