[PDF] Top 20 Few Shot and Zero Shot Learning for Historical Text Normalization

... curves that have been micro-averaged over all ten datasets, but evaluated on different subsets of the data: (a) tokens that have been seen during train- ing (“knowns”) or not (“unknowns”); and (b) to- kens that stay ... See full document

... efficient learning, few- shot learning techniques are being actively ...The zero-shot learning (ZSL) paradigm represents the extreme case of few-shot ... See full document

... Figure 1: Framework of embedding based ZSL approaches. using the labeled samples becomes an important and practi- cal problem and has gathered considerable research interests from the machine learning and computer ... See full document

... Zero-shot learning (ZSL) and cold-start recommendation (CSR) are two challenging problems in computer vision and recommender system, respectively. In general, they are inde- pendently investigated in ... See full document

... The early ZSL work has a limitation that the learned model only can differentiate categories between unseen classes, which violates the reality. Recently, [17, 26] extend ZSL to a more general scene called Generalized ... See full document

... for zero-shot learning, in order to achieve a better understanding of its shortcom- ings, and improve its quality by devising meth- ods to overcome ...reporting zero-shot performances ... See full document

... Zero-shot learning (ZSL) is to build recognition models for previously unseen target classes which have no labeled data for training by transferring knowledge from some other re- lated auxiliary ... See full document

... Code.org is an online education platform for teaching begin- ners fundamental concepts in programming. Students build their solutions in a drag-and-drop interface that pieces to- gether blocks of code. Its growing ... See full document

... both built using unsupervised word embeddings and share these embedding parameters, the model can address the issues of domain adaptation. Word embeddings capture word similarities, and hence the classifier is able to ... See full document

... 4. Strength of the constraint. We assume this to be specified by a quantifier. For our running ex- ample, this corresponds to the adverb ‘usually’. In this work, by quantifier we specifically refer to frequency adverbs ... See full document

... deep learning (DL) approaches for OOD detection and ID classifica- tion task often require massive amounts of ID or OOD labeled data (Kim and Kim, ...(i.e., few-shot learning) and no OOD ... See full document

... In this work, we take a fresh look at the event ex- traction task and model it as a generic ground- ing problem. We propose a transferable neu- ral architecture, which leverages existing human- constructed event schemas ... See full document

... including text classifica- tion. Recognising text documents of classes that have never been seen in the learning stage, so-called zero-shot text classification, is there- fore ... See full document

... towards learning human language? Motivated by this ques- tion, we aim at constructing an informa- tive prior for held-out languages on the task of character-level, open-vocabulary language ...both ... See full document

... We randomly sampled data from the dataset for each division in Table 1 without any overlap; we ignored the modality of each document that was not available in each data division (e.g., Japanese text in ... See full document

... a zero-shot semantic parsing task: parsing instructions into compositional logical forms, in domains that were not seen during ...support zero-shot adaptation. Our experiments with various ... See full document

... Zero-shot text classification (0 SHOT - TC ) is a challenging NLU problem to which little at- tention has been paid by the research com- ...0 SHOT - TC aims to associate an ap- ... See full document

... V.G.Vinod Vydiswaran, Grace Ganzel, Bryan Romas, Deahan Yu, Amy Austin, Neha Bhomia, Socheatha Chan, Stephanie Hall, Van Le, Aaron Miller, Olawunmi Oduyebo, Aulia Song, Radhika Sondhi, Danny Teng, Hao Tseng, Kim Vuong ... See full document

... Few-shot learning is devoted to resolving the data deficiency problem by recognizing novel classes from very few labeled ...very few examples challenges the standard fine-tuning method ... See full document

... accurate learning methods based on the continuous ...deep learning models are dedicated to extracting embedding features from large scale data which belong to the same distribution and use them to classify ... See full document