Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence Labeler
We propose a neuralized undirected graphical model called Neural-Hidden-CRF to solve the weakly-supervised sequence labeling problem. Under the umbrella of probabilistic undirected graph theory, the proposed Neural-Hidden-CRF embedded with a hidden CRF layer models the variables of word sequence, latent ground truth sequence, and weak label sequence with the global perspective that undirected graphical models particularly enjoy. In Neural-Hidden-CRF, we can capitalize on the powerful language model BERT or other deep models to provide rich contextual semantic knowledge to the latent ground truth sequence, and use the hidden CRF layer to capture the internal label dependencies. Neural-Hidden-CRF is conceptually simple and empirically powerful. It obtains new state-of-the-art results on one crowdsourcing benchmark and three weak-supervision benchmarks, including outperforming the recent advanced model CHMM by 2.80 F1 points and 2.23 F1 points in average generalization and inference performance, respectively.
Code (1)
Tasks
Language ModelingLanguage ModellingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A weakly supervised sequence tagging and grammar induction approach to semantic frame slot filling
This paper describes continuing work on semantic frame slot filling for a command and control task using a weakly-supervised approach. We investigate the advantages of using retraining techniques that take the output of …
slot-fillingSlot FillingBART-TL: Weakly-Supervised Topic Label Generation
We propose a novel solution for assigning labels to topic models by using multiple weak labelers. The method leverages generative transformers to learn accurate representations of the most important topic terms and candi…
Topic ModelsBSNet: Box-Supervised Simulation-assisted Mean Teacher for 3D Instance Segmentation
3D instance segmentation (3DIS) is a crucial task, but point-level annotations are tedious in fully supervised settings. Thus, using bounding boxes (bboxes) as annotations has shown great potential. The current mainstrea…
3D Instance SegmentationDecoderInstance SegmentationSemantic SegmentationGeneralized Weak Supervision for Neural Information Retrieval
Neural ranking models (NRMs) have demonstrated effective performance in several information retrieval (IR) tasks. However, training NRMs often requires large-scale training data, which is difficult and expensive to obtai…
Information RetrievalPassage RetrievalRetrievalVariational Sequential Labelers for Semi-Supervised Learning
We introduce a family of multitask variational methods for semi-supervised sequence labeling. Our model family consists of a latent-variable generative model and a discriminative labeler. The generative models use latent…
Learning Word EmbeddingsWord Embeddings