[PDF] Top 20 Neural Sequence to sequence Learning of Internal Word Structure
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Neural Sequence to sequence Learning of Internal Word Structure
... It can be seen in Table 2 that cSMT outper- forms cED in the token regime. One possible ex- planation for this outcome is that the inclusion of the word counts helps to learn the character align- ments better. ... See full document
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Neural Sequence Learning Models for Word Sense Disambiguation
... that neural sequence learning rep- resents a novel and effective alternative to the tra- ditional way of modeling supervised WSD, en- abling a single all-words model to compete with a pool of ... See full document
12
Bandit Structured Prediction for Neural Sequence to Sequence Learning
... a word-level maximum likelihood ...many sequence-to-sequence learning tasks, the resulting models suffer from exposure bias, since they learn to generate output words based on the history of ... See full document
11
Learning to Summarize Radiology Findings
... and neural baselines on our dataset measured by the standard ROUGE ...with neural sequence-to-sequence learning, and to our knowl- edge our work represents the first attempt in this ... See full document
10
Contextualized Non-Local Neural Networks for Sequence Learning
... Table 3: Performance of the proposed models on all datasets compared to typical baselines. × indicates that corresponding models can not work since the sentences are too long to be processed by parser. ∗ denotes ... See full document
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Sequence-to-sequence modeling for graph representation learning
... In the unsupervised group, existing graph comparison methods can be categorized into three main (not necessarily disjoint) classes: feature extraction, graph kernels, and graph matching. Feature extraction methods ... See full document
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Sparse Coding of Neural Word Embeddings for Multilingual Sequence Labeling
... for learning distributed word represen- tations for various specific language analysis ...propose neural network archi- tectures to four natural language processing tasks, ...train word ... See full document
16
Computational Ad Hominem Detection
... machine learning approach to classify ad hominem ...two sequence models: a bidirec- tional GRU neural network for a sequence of word representations and another similar network for POS ... See full document
7
Graph to Sequence Learning using Gated Graph Neural Networks
... graph-to- sequence (henceforth, g2s) learning that lever- ages recent advances in neural encoder-decoder ...Graph Neural Networks (Li et ...graph structure without loss of ... See full document
11
Sequence to Sequence Learning as Beam Search Optimization
... and sequence-labeling tasks. Seq2seq builds on deep neural language modeling and inherits its remarkable accuracy in estimating local, next-word ...different sequence to sequence tasks: ... See full document
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Simple Models for Word Formation in Slang
... of learning to blend and derive data-driven com- putational models for the ...length sequence to sequence learning problem and propose a neural encoder-decoder based ... See full document
11
Deep Neural Models for Medical Concept Normalization in User Generated Texts
... a sequence learning problem with powerful neural networks such as recurrent neural networks and contextual- ized word representation models trained to ob- tain semantic representations ... See full document
7
Self Regulated Interactive Sequence to Sequence Learning
... of the reward statistics and has no internal con- textual state representation. The comparison of Reg3 with -greedy for a range of values for in Figure 5 shows that learned regulator behaves indeed very similar to ... See full document
13
Neural AMR: Sequence to Sequence Models for Parsing and Generation
... Sequence-to-sequence models have shown strong performance across a broad range of applications. However, their applica- tion to parsing and generating text using Abstract Meaning Representation (AMR) has ... See full document
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Incorporating Copying Mechanism in Sequence to Sequence Learning
... in sequence-to-sequence (Seq2Seq) learning referred to as copying, in which cer- tain segments in the input sequence are selectively replicated in the output se- ...into neural network- ... See full document
10
Mark my Word: A Sequence to Sequence Approach to Definition Modeling
... a sequence- to-sequence task, rather than a word-to- sequence task: given an input sequence with a highlighted word, generate a con- textually appropriate definition for ...based ... See full document
11
Constrained Sequence to sequence Semitic Root Extraction for Enriching Word Embeddings
... the word; this is due to the non-concatenative templatic process whereby morphemes are inserted between char- acters of the root as part of the word formation ...the word as some root characters can ... See full document
9
A Neural, Interactive predictive System for Multimodal Sequence to Sequence Tasks
... The NMT task regards the translation of texts from a medical domain. The system is similar to the one used by Peris and Casacuberta (2019), and was trained on the UFAL corpus (Bojar et al., 2017). The image and video ... See full document
6
A Hierarchical Word Sequence Language Model
... instance, in the sentence ’Mrs. Allen is a senior ed- itor of insight magazine’, ’of’ is the most frequently used word in Table 2, then we use ’of’ to divide this sentence into ’Mrs. Allen is a senior editor’ and ... See full document
6
An Exploration of Neural Sequence to Sequence Architectures for Automatic Post Editing
... Both {mt, src} → pe systems take advantage of the src information and improve the input. The proposed modifications could be accepted as cor- rect; one matches the reference. The highlighted rows and columns in Figure 2 ... See full document
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