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Extracting Event Temporal Relations via Hyperbolic Geometry ...
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FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging ...
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Open Aspect Target Sentiment Classification with Natural Language Prompts ...
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Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you? ...
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We've had this conversation before: A Novel Approach to Measuring Dialog Similarity ...
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ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations ...
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CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization ...
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Partially Supervised Named Entity Recognition via the Expected Entity Ratio Loss ...
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Abstract:
We study learning named entity recognizers in the presence of missing entity annotations. We approach this setting as tagging with latent variables and propose a novel loss, the Expected Entity Ratio, to learn models in the presence of systematically missing tags. We show that our approach is both theoretically sound and empirically useful. Experimentally, we find that it meets or exceeds performance of strong and state-of-the-art baselines across a variety of languages, annotation scenarios, and amounts of labeled data. In particular, we find that it significantly outperforms the previous state-of-the-art methods from [Mayhew et al. '19] and [Li et al. '21] by +12.7 and +2.3 F1 score in a challenging setting with only 1,000 biased annotations, averaged across 7 datasets. We also show that, when combined with our approach, a novel sparse annotation scheme outperforms exhaustive annotation for modest annotation budgets. We have published our implementation and experimental results at ...
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Keyword:
Computational Linguistics; Information Extraction; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
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URL: https://dx.doi.org/10.48448/xhsd-jp35 https://underline.io/lecture/38200-partially-supervised-named-entity-recognition-via-the-expected-entity-ratio-loss
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Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention ...
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Analyzing the Surprising Variability in Word Embedding Stability Across Languages ...
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Neural Machine Translation with Heterogeneous Topic Knowledge Embeddings ...
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Towards Zero-Shot Knowledge Distillation for Natural Language Processing ...
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SIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal Conversations ...
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Automatic Text Evaluation through the Lens of Wasserstein Barycenters ...
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Combining sentence and table evidence to predict veracity of factual claims using TaPaS and RoBERTa ...
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