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Adapting BigScience Multilingual Model to Unseen Languages ...
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On Efficiently Acquiring Annotations for Multilingual Models ...
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Team ÚFAL at CMCL 2022 Shared Task: Figuring out the correct recipe for predicting Eye-Tracking features using Pretrained Language Models ...
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Does Corpus Quality Really Matter for Low-Resource Languages? ...
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IIITDWD-ShankarB@ Dravidian-CodeMixi-HASOC2021: mBERT based model for identification of offensive content in south Indian languages ...
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mSLAM: Massively multilingual joint pre-training for speech and text ...
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On the Representation Collapse of Sparse Mixture of Experts ...
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Politics and Virality in the Time of Twitter: A Large-Scale Cross-Party Sentiment Analysis in Greece, Spain and United Kingdom ...
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L3Cube-MahaHate: A Tweet-based Marathi Hate Speech Detection Dataset and BERT models ...
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Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts ...
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A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language Model ...
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A New Generation of Perspective API: Efficient Multilingual Character-level Transformers ...
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Factual Consistency of Multilingual Pretrained Language Models ...
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Examining Scaling and Transfer of Language Model Architectures for Machine Translation ...
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MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset ...
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Mono vs Multilingual BERT for Hate Speech Detection and Text Classification: A Case Study in Marathi ...
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Natural Language Descriptions of Deep Visual Features ...
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Abstract:
Some neurons in deep networks specialize in recognizing highly specific perceptual, structural, or semantic features of inputs. In computer vision, techniques exist for identifying neurons that respond to individual concept categories like colors, textures, and object classes. But these techniques are limited in scope, labeling only a small subset of neurons and behaviors in any network. Is a richer characterization of neuron-level computation possible? We introduce a procedure (called MILAN, for mutual-information-guided linguistic annotation of neurons) that automatically labels neurons with open-ended, compositional, natural language descriptions. Given a neuron, MILAN generates a description by searching for a natural language string that maximizes pointwise mutual information with the image regions in which the neuron is active. MILAN produces fine-grained descriptions that capture categorical, relational, and logical structure in learned features. These descriptions obtain high agreement with ... : To be published as a conference paper at ICLR 2022 ...
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Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; Computer Vision and Pattern Recognition cs.CV; FOS Computer and information sciences; Machine Learning cs.LG
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URL: https://arxiv.org/abs/2201.11114 https://dx.doi.org/10.48550/arxiv.2201.11114
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From Examples to Rules: Neural Guided Rule Synthesis for Information Extraction ...
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