[PDF] Top 20 SC LSTM: Learning Task Specific Representations in Multi Task Learning for Sequence Labeling
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SC LSTM: Learning Task Specific Representations in Multi Task Learning for Sequence Labeling
... quence labeling has been studied for ...for sequence labeling tasks, such as LSTM-CNN (Chiu and Nichols, 2015), LSTM-CRF (Huang et ...and LSTM-CNN-CRF (Ma and Hovy, 2016). These ... See full document
11
Sequence Labeling Parsing by Learning across Representations
... SPMRL We also use the SPMRL datasets, a col- lection of parallel dependency and constituency treebanks for morphologically rich languages (Seddah et al., 2014). In this case, we use the predicted PoS tags provided by the ... See full document
8
Dependency Parsing as Sequence Labeling with Head Based Encoding and Multi Task Learning
... In general, long dependencies are especially difficult to predict correctly. While local dependencies (neighbouring child) achieve more than 80% UAS on average, dependencies of length more than 6 do not overcome 50% UAS ... See full document
8
Learning Multi-Task Communication with Message Passing for Sequence Learning
... Table 1 shows the overall results on the 16 different tasks under three settings: single task, multiple task, and transfer learning. Generally, we can see that almost all tasks benefit from ... See full document
8
Auxiliary Objectives for Neural Error Detection Models
... the sequence labeling model with additional linguistic informa- tion, allowing it to learn useful compositional fea- tures that can then be exploited for error detec- ...of multi-task ... See full document
11
Identifying beneficial task relations for multi task learning in deep neural networks
... of sequence labeling with deep recurrent neural ...single task architectures are reused in the multi-task set- up (no additional tuning), which makes predict- ing gains ...90 ... See full document
6
A Multi task Approach to Learning Multilingual Representations
... bi-directional LSTM (Hochreiter et ...the LSTM outputs (Figure ...The LSTM outputs (hidden states) contextualize input word embeddings by encod- ing the history of each word into its represen- ... See full document
7
SRL4ORL: Improving Opinion Role Labeling Using Multi Task Learning with Semantic Role Labeling
... the task is usually approached with sequence labeling techniques and the BIO encoding scheme (Choi et ...an LSTM-based joint model was proposed (Katiyar and Cardie, 2016) that unlike the prior ... See full document
12
A Syntax aware Multi task Learning Framework for Chinese Semantic Role Labeling
... 3) learning the syn- tactic features with the main task performs better than extract them from a fixed dependency ...BERT representations or ...BERT representations or not, ... See full document
11
A Multi lingual Multi task Architecture for Low resource Sequence Labeling
... of sequence labeling is to assign a categorical label to each token in a given sen- ...on sequence labeling tasks, they typically relied on hand-crafted features, therefore it is difficult to ... See full document
11
Multi Task Active Learning for Neural Semantic Role Labeling on Low Resource Conversational Corpus
... role labeling and entity ...primary task (SRL) (Zhou and Xu, 2015) and softmax layer for the auxiliary ...auxiliary task acts as a regularization method (Caruana, ... See full document
8
Multi-Stage Multi-Task Feature Learning
... otherwise, no penalty is imposed. In other words, MSMTFL in the current stage tends to shrink the small rows of W and keep the large rows of W in the last stage. However, Lasso (corresponds to ℓ = 1) penalizes all rows ... See full document
32
Multi Task Learning for Coherence Modeling
... Deep learning architec- tures have also been successfully applied to the task of coherence scoring, achieving state-of-the- art results (Li and Jurafsky, 2017; Logeswaran et ... See full document
11
A review on multi-task metric learning
... proposed multi-task maximally collapsing metric learning to solve the person re-identification over camera ...a multi-task learning approach for this ...the ... See full document
23
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
... for multi-task learning that take advantage of natural groupings of re- lated ...tasks. Task groups may be defined along known properties of the tasks, such as task do- main or ...Such ... See full document
11
Code Switching Language Modeling using Syntax Aware Multi Task Learning
... setting. Learning at the same time syntactic fea- tures such as POS tag and language identifier al- lows to have a shared grammatical information that constraint the next word ...a multi-task ... See full document
6
Deep Automated Multi task Learning
... Multi-task learning (MTL) has recently contributed to learning better representa- tions in service of various NLP ...primary task, by jointly training on a secondary ...primary ... See full document
6
A sequence learning impairment in dyslexia? It depends on the task
... the sequence learning effects obtained from the SRT task (Howard et ...that sequence learning may be reflective of the extent to which phoneme-grapheme mappings are consolidated when ... See full document
39
Investigating Meta Learning Algorithms for Low Resource Natural Language Understanding Tasks
... apply multi-task learning to representation learning (Liu et ...2015). Multi-task learn- ing allows the model to leverage supervision sig- nals from related tasks and prevents ... See full document
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