[PDF] Top 20 Multi Task Active Learning for Linguistic Annotations
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Multi Task Active Learning for Linguistic Annotations
... Supervised machine learning methods have success- fully been applied to many NLP tasks in the last few decades. These techniques have demonstrated their superiority over both hand-crafted rules and unsu- pervised ... See full document
9
Sequential Multi Task Spectral Clustering Scheme with Active Learning paradigm
... inter task correlations are identified in the unsupervised way in the random matched correlations, so that the clusters labels are most ...previous multi task clustering ... See full document
6
Exploiting Linguistic Resources for Neural Machine Translation Using Multi task Learning
... For all our experiments, we use an attentional encoder-decoder model. The baseline systems use this architecture as well. The encoder uses word embeddings of size 256 and a bidirectional LSTM (Hochreiter and Schmidhuber, ... See full document
10
Investigating the Role of Argumentation in the Rhetorical Analysis of Scientific Publications with Neural Multi Task Learning Models
... manual annotations of several high-level rhetorical aspects of scientific writing ...without annotations of the argumentative structure of publi- ... See full document
13
Multi-domain and multi-task prediction of extraversion and leadership from meeting videos
... and multi-task learning methods in conjunction with the non-verbal features and crowd-sourced annotations from the Video bLOG (VLOG) corpus to perform a multi-domain and ... See full document
14
Analysis of Automatic Annotation Suggestions for Hard Discourse Level Tasks in Expert Domains
... and active learning for entities and chunks generated by logistic regression and ...entity annotations based on a recurrent neural net- ...of active learning, enabling continuous up- ... See full document
12
Linguistic representations in multi task neural networks for ellipsis resolution
... with multi-task learning are able to achieve comparable results to Anand and Hardt, without relying on structured syntactic annotation or hand- crafted ... See full document
8
Comparing linguistic information in treebank annotations
... In this section we present an experiment that refers to the automatic acquisition of lexical knowledge about Verbs, and more precisely sub-categorization frames. Since the annotation of TUT is especially centered on ... See full document
6
Multi Task Learning of Keyphrase Boundary Classification
... target annotations from FrameNet ...of multi-word ex- pressions using the Streusle corpus (Schneider and Smith, 2015); and (5) semantic super-sense tagging using the Semcor dataset, following Jo- hannsen et ... See full document
6
Multi Task Learning for Coherence Modeling
... dependencies between the two prediction tasks and achieve state-of-the-art results in predicting document-level coherence; (2) We assess the ex- tent to which the information encoded in the net- work generalizes to ... See full document
11
Active Learning with Multiple Annotations for Comparable Data Classification Task
... traditional active learning setup that is suit- able for eliciting a single ...of annotations - class label as- signment and parallel segment extraction and pro- pose strategies in active ... See full document
9
Learning Hierarchical Translation Structure with Linguistic Annotations
... translation task, we performed further experiments along two ...the linguistic annotations, by comparing our complete system (lts) with an otherwise identical implementation (lts-nolabels) which does ... See full document
11
Multi Task Active Learning for Neural Semantic Role Labeling on Low Resource Conversational Corpus
... Our model is a modification of He et al.’s work. Our first adjustment is to use CRF as the last layer instead of softmax because of its notable superi- ority found by Reimers and Gurevych (2017) for both role labeling ... See full document
8
AutoSeM: Automatic Task Selection and Mixing in Multi Task Learning
... task. Multi-task learning has been applied to a wide range of natural language processing prob- lems (Luong et ...a multi- task learning system is ...also learning ... See full document
12
Multi Task Networks with Universe, Group, and Task Feature Learning
... that task structure is usually un- clear, Evgeniou and Pontil (2004) extended sup- port vector machines for single-task learning in a multi-task scenario by penalizing models if they ... See full document
11
Bounds for Linear Multi-Task Learning
... of multi-task learning, but a trivial consequence of the fact that we estimate an average of m probabilities (in contrast to Ben David, 2003, where bounds are valid for each individual task - ... See full document
23
Multi Domain Adaptation for SMT Using Multi Task Learning
... In this paper, we use MTL to jointly adapt SMT models to multiple domains. Specifically, we de- velop multiple SMT systems based on mixture mod- els, where each system is tailored for one specific domain with an ... See full document
11
SC LSTM: Learning Task Specific Representations in Multi Task Learning for Sequence Labeling
... each task sepa- ...tagging task. They do report gains on both target tasks over single task models, but results varied depending where the additional task was taken care of in their ... See full document
11
Multi task Learning for Natural Language Generation in Task Oriented Dialogue
... modeling task on human- generated responses as an unconditioned comple- mentary process that brings in more language- related elements, without the intervention of re- quired MR ...under multi-task ... See full document
6
Investigating Meta Learning Algorithms for Low Resource Natural Language Understanding Tasks
... There is a long history of learning general lan- guage representations. Previous work on learn- ing general language representations focus on learning word (Mikolov et al., 2013; Pennington et al., 2014) or ... See full document
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