A Study on the Autoregressive and non-Autoregressive Multi-label Learning
Extreme classification tasks are multi-label tasks with an extremely large number of labels (tags). These tasks are hard because the label space is usually (i) very large, e.g. thousands or millions of labels, (ii) very sparse, i.e. very few labels apply to each input document, and (iii) highly correlated, meaning that the existence of one label changes the likelihood of predicting all other labels. In this work, we propose a self-attention based variational encoder-model to extract the label-label and label-feature dependencies jointly and to predict labels for a given input. In more detail, we propose a non-autoregressive latent variable model and compare it to a strong autoregressive baseline that predicts a label based on all previously generated labels. Our model can therefore be used to predict all labels in parallel while still including both label-label and label-feature dependencies through latent variables, and compares favourably to the autoregressive baseline. We apply our models to four standard extreme classification natural language data sets, and one news videos dataset for automated label detection from a lexicon of semantic concepts. Experimental results show that although the autoregressive models, where use a given order of the labels for chain-order label prediction, work great for the small scale labels or the prediction of the highly ranked label, but our non-autoregressive model surpasses them by around 2% to 6% when we need to predict more labels, or the dataset has a larger number of the labels.
Code (0)
등록된 구현이 없습니다.
Tasks
Multi-Label LearningSimilar Papers 제목 키워드 기반
Utilizing Multiple Inputs Autoregressive Models for Bearing Remaining Useful Life Prediction
Accurate prediction of the Remaining Useful Life (RUL) of rolling bearings is crucial in industrial production, yet existing models often struggle with limited generalization capabilities due to their inability to fully …
Neural Diarization with Non-autoregressive Intermediate Attractors
End-to-end neural diarization (EEND) with encoder-decoder-based attractors (EDA) is a promising method to handle the whole speaker diarization problem simultaneously with a single neural network. While the EEND model can…
Decoderspeaker-diarizationSpeaker DiarizationNon-Autoregressive Image Captioning with Counterfactuals-Critical Multi-Agent Learning
Most image captioning models are autoregressive, i.e. they generate each word by conditioning on previously generated words, which leads to heavy latency during inference. Recently, non-autoregressive decoding has been p…
Image CaptioningMachine TranslationMulti-agent Reinforcement LearningSentence+1Improving Transfer Learning for Sequence Labeling Tasks by Adapting Pre-trained Neural Language Models
This doctoral thesis improves the transfer learning for sequence labeling tasks by adapting pre-trained neural language models. The proposed improvements in transfer learning involve introducing a multi-task model that i…
Transfer LearningDeep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation
Much recent effort has been invested in non-autoregressive neural machine translation, which appears to be an efficient alternative to state-of-the-art autoregressive machine translation on modern GPUs. In contrast to th…
DecoderKnowledge DistillationMachine TranslationTranslation