[PDF] Top 20 Weakly Supervised Natural Language Learning Without Redundant Views
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Weakly Supervised Natural Language Learning Without Redundant Views
... Although co-training produces substantial improve- ments over the baseline at its best parameter settings, a closer examination of our results reveals that they cor- roborate previous findings: the algorithm is sensitive ... See full document
8
Deep Residual Learning for Weakly Supervised Relation Extraction
... residual learning (ResNet) (He et ...ual learning on noisy natural language pro- cessing tasks is still not well ...ual learning, and investigate its impacts on the task of distantly ... See full document
5
Weakly Supervised Learning of Presupposition Relations between Verbs
... of natural language is that significant portions of content conveyed in a mes- sage may not be overtly ...many natural language processing applications, such as information ex- traction, text ... See full document
6
Weakly supervised learning of allomorphy
... of natural language processing, mor- phological segmentation is a well-researched and established problem (Goldsmith (2001), Creutz and Lagus (2005), Poon et ... See full document
11
Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions
... which language is used pro- vides a strong signal for learning to recover its ...executing natural language instructions, using various types of weak ... See full document
14
A Weakly Supervised Learning Approach for Spoken Language Understanding
... a natural language corpus was collected through a specific website which simulated a dialog ...2,286 natural lan- guage utterances through this ...spoken language corpus was collected through ... See full document
9
WSLLN:Weakly Supervised Natural Language Localization Networks
... instance learning and iterative classifer refinement in a single ...for weakly super- vised object detection (Gao et ...segments, weakly su- pervised action detectors use weaker annotations, ... See full document
7
Weakly Supervised Learning for Hedge Classification in Scientific Literature
... into weakly supervised ML techniques for NLP ...multiple redundant (or semi-redundant) ‘views’ of a data sample and per- form mutual ... See full document
8
Learning Representations for Weakly Supervised Natural Language Processing Tasks
... There is a long tradition of NLP research on representations, mostly falling into one of four categories: 1) vector space models of meaning based on document-level lexical co- occurrence statistics (Salton and McGill ... See full document
36
Recognizing Explicit and Implicit Hate Speech Using a Weakly Supervised Two path Bootstrapping Approach
... The error of our model comes from semantic drift in bootstrapping learning, which partially results from the complexity and dynamics of language. Specifically, we found dynamic word sense of slurs and ... See full document
9
Adversarial Learning for Weakly-Supervised Social Network Alignment
... Different of previous semi-supervised works, we view all the identities in a social network as a whole and perform identity alignment in the distribution level. As shown in Fig- ure 1, the distributions of ... See full document
8
Weakly Supervised Learning with Cost Augmented Contrastive Estimation
... However, when doing POS induction without a tag dictionary, the tags are simply unique identi- fiers and may not have consistent meaning across runs. To address this, we propose a novel voting scheme that is ... See full document
13
Weakly Supervised Spatio Temporally Grounding Natural Sentence in Video
... namely weakly-supervised spatio-temporally video grounding (WSSTG), which localizes a spatio- temporal tube in a given video that semantically corresponds to a given natural sentence, in a ... See full document
11
Weak Supervision for Learning Discourse Structure
... structure learning problem to predict- ing edges or attachments between discourse unit (DU) pairs in the dependency ...a supervised deep learning algorithm to pre- dict attachments on the STAC ... See full document
10
Multi Task Transfer Learning for Weakly Supervised Relation Extraction
... a weakly-supervised relation extrac- tion problem, leveraging both labeled instances of auxiliary relation types and human knowledge in- cluding hypotheses on feature generality and en- tity type ... See full document
9
Cross lingual Projected Expectation Regularization for Weakly Supervised Learning
... Our work is also closely related to Ganchev et al. (2009). They used a two-step projection method similar to Das and Petrov (2011) for dependency parsing. Instead of using the projected linguis- tic structures as ground ... See full document
12
Proceedings of the NAACL HLT 2009 Workshop on Semi supervised Learning for Natural Language Processing
... semi-supervised learning? Recent learning paradigms such as constraint-driven learning and prototype learning take advantage of our domain knowledge about particular NLP tasks; they ... See full document
10
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
... of learning uni- versal representations of sentences, ...on learning word embeddings, most current approaches consider learning sen- tence encoders in an unsupervised manner like SkipThought (Kiros ... See full document
11
A Supertag Context Model for Weakly Supervised CCG Parser Learning
... real-world learning scenarios will always lack complete knowledge of the lexicon, we, too, want to allow for unknown words; for these, we assume the word may take any known ... See full document
10
Semi Supervised Neural Text Generation by Joint Learning of Natural Language Generation and Natural Language Understanding Models
... a learning scheme which provides the ability to jointly learn two mod- els for NLG and for NLU using large amount of unannotated data and small amount of anno- tated ...and without using any pre-processing ... See full document
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