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A Multi task Approach to Learning Multilingual Representations
... ing multilingual text representations shared across languages (Faruqui and Dyer, 2014; Bengio and Corrado, 2015; Luong et ...2015). Multilingual embeddings open up the possibility of transferring ... See full document
7
CoNLL 2016 Shared Task on Multilingual Shallow Discourse Parsing
... sub- task, there is a good balance between “con- ventional” machine learning techniques such as Support Vector Machines and Maximum Entropy models that rely heavily on hand- crafted features, and neural ... See full document
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Learning Joint Multilingual Sentence Representations with Neural Machine Translation
... We can make the following observations. First, using an BLSTM with max-pooling (Table 1 right) performs much better than an LSTM and us- ing the last hidden state as sentence representa- tion (Table 1 left). This was ... See full document
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Cross lingual Transfer Learning for Multilingual Task Oriented Dialog
... Despite the range of models that we consid- ered in this paper, we only scratched at the sur- face of possible cross-lingual (embedding) mod- els, and hence there are many future directions of this work. First, except ... See full document
11
Multi task Learning for Multi modal Emotion Recognition and Sentiment Analysis
... better multi-modal feature representa- tion when these modalities from the context are combined with the modalities of the target utter- ...proposed approach on the recent benchmark dataset of CMU-MOSEI ... See full document
10
A Multi classifier Approach to support Coreference Resolution in a Vector Space Model
... machine learning ap- proach is presented to deal with the coref- erence resolution ...This approach con- sists of a multi-classifier system that classifies mention-pairs in a reduced dimensional ... See full document
8
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
Learning Cross Lingual Sentence Representations via a Multi task Dual Encoder Model
... size of 100 using stochastic gradient descent with a learning rate of 0.008. All of our models are trained for 30 million steps. All input text is tree- bank style tokenized prior to being used for train- ing. We ... See full document
10
A Multi task Learning Approach to Adapting Bilingual Word Embeddings for Cross lingual Named Entity Recognition
... a multi-task learning approach can help adapt bilingual word embeddings (BWE’s) to improve cross-lingual ...be task- specific, and outperforms the baseline of using pre-trained ... See full document
6
A Multi Task Architecture on Relevance based Neural Query Translation
... a multi-task learning approach to train a Neural Machine Translation (NMT) model with a Relevance-based Auxiliary Task (RAT) for search query ...(CLIR) task is usually treated as ... See full document
6
Lexicon information in neural sentiment analysis: a multi task learning approach
... learn task-specific information (which words convey sentiment, how to resolve negation, how to resolve intensification) in a data-driven manner (Socher et ... See full document
12
Cross lingual Learning of Semantic Textual Similarity with Multilingual Word Representations
... word representations allow us to leverage more available data for multilingual learning of semantic textual ...how multilingual character-level representations can be included, perhaps ... See full document
5
A Hierarchical Multi-Task Approach for Learning Embeddings from Semantic Tasks
... a multi-task architecture combining four different tasks that have not been explored together to the best of our knowl- ...for multi-task learning, proportional ...of ... See full document
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DUT NLP at MEDIQA 2019: An Adversarial Multi Task Network to Jointly Model Recognizing Question Entailment and Question Answering
... Adversarial Multi- Task Network to jointly model RQE and QA shared ...shared representations across the two ...shared representations of both tasks provided by multi-task ... See full document
9
UTFPR at SemEval 2019 Task 5: Hate Speech Identification with Recurrent Neural Networks
... We approach the task us- ing a system based on minimalistic compo- sitional Recurrent Neural Networks ...our approach on the SemEval-2019 Task 5: Multilingual Detection of Hate Speech ... See full document
5
Multi Head Attention with Diversity for Learning Grounded Multilingual Multimodal Representations
... image. Multi-head attention with diversity: We em- ploy K-head attention networks to attend to the visual objects in an image as well as the tex- tual semantics in a sentence then generate fixed- length ... See full document
7
A Multi Task Approach for Disentangling Syntax and Semantics in Sentence Representations
... There is a growing amount of work on learning in- terpretable or disentangled latent representations both in machine learning (Tenenbaum and Free- man, 2000; Reed et al., 2014; Makhzani et al., 2015; ... See full document
12
Learning representations for sentiment classification using Multi task framework
... a Multi- task learning ...the multi- task model. We validate the representations on an independent test Irony dataset that can contain several sentiments within each sample, with ... See full document
10
Multilingual NMT with a Language Independent Attention Bridge
... sentence representations learned by our model to downstream tasks collected in the SentEval toolkit (Conneau and Kiela, 2018) to evaluate the quality of our language-agnostic sen- tence ... See full document
7
Multi Task Active Learning for Neural Semantic Role Labeling on Low Resource Conversational Corpus
... steep learning curve even for annotators with a linguistic back- ...Answering-driven approach by casting a predicate as a question and its thematic role as an answer in the ...active learning using ... See full document
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