[PDF] Top 20 Neural AMR: Sequence to Sequence Models for Parsing and Generation
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Neural AMR: Sequence to Sequence Models for Parsing and Generation
... The model uses a stacked bidirectional-LSTM encoder to encode an input sequence and a stacked LSTM to decode from the hidden states produced by the encoder. We make two modifications to the encoder: (1) we ... See full document
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Assessing Incrementality in Sequence to Sequence Models
... 2018), parsing (K¨ohn and Menzel, 2014), or in other problems that are nat- urally equipped with time constraints like real- time neural machine translation (Neubig et ...in ... See full document
9
Addressing the Data Sparsity Issue in Neural AMR Parsing
... original AMR graph from its linearized sequence. The sequence also captures the branching information of each rela- tion explicitly by representing it with a start sym- bol and an end symbol specific ... See full document
10
The Unbearable Weight of Generating Artificial Errors for Grammatical Error Correction
... recent neural sequence-to- sequence models—(i) multi-layer convolutional model (MLCONV; Chollampatt and Ng, 2018), (ii) Transformer (Vaswani et ...(iii) Parsing-Reading-Predict Networks ... See full document
6
Controlling Sequence to Sequence Models A Demonstration on Neural based Acrostic Generator
... We show that by appending additional informa- tion in the training input sequences, it is possi- ble to train a Seq2Seq model whose outputs can be controlled in a fine-grained level. This find- ing enables us to design ... See full document
6
Sequence to Action: End to End Semantic Graph Generation for Semantic Parsing
... RNN models have achieved success in sequence-to-sequence problems due to its strong ability on both representation learning and prediction, ...Seq2Seq models have also been employed for ... See full document
12
Structural Neural Encoders for AMR to text Generation
... From the test split of LDC2017T10, we gener- ated 251 contrastive examples due to antecedent replacements, 912 due to pronoun type replace- ments, 1840 due to number replacements and 95 due to gender replacements. 5 The ... See full document
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Unlabeled Data for Morphological Generation With Character Based Sequence to Sequence Models
... and a target tag t. W denotes a set of words in the language of the system. The encoding function e depends on θ. The parameters θ are shared across the two tasks, resulting in a share of information. We obtain this by ... See full document
6
Deep Learning Approach for Text Generation Using RNN Encoder-Decoder for Q&A
... of neural network based on encoder and decoder for machine translation and new type of cell called Gated Recurrent Unit ...The neural network proposed in article encodes source language sentence in fix ... See full document
6
Attending to Characters in Neural Sequence Labeling Models
... low parsing can be framed as types of sequence ...efficient sequence labeling models is thereby useful for a wide range of downstream ...in neural architectures and representation ... See full document
10
Grammatical Sequence Prediction for Real Time Neural Semantic Parsing
... While sequence-to-sequence (seq2seq) mod- els achieve state-of-the-art performance in many natural language processing tasks, they can be too slow for real-time ...seq2seq models for semantic ... See full document
10
MT/IE: Cross lingual Open Information Extraction with Neural Sequence to Sequence Models
... of neural models in machine translation (Kalchbrenner and Blun- som, 2013; Cho et ...syntactic parsing (Vinyals et ...a sequence-to-sequence model that enables end-to- end cross-lingual ... See full document
7
A Graph to Sequence Model for AMR to Text Generation
... the sequence-to-sequence model (Sutskever et ...on AMR-to-text gen- eration (Konstas et ...of sequence-to-sequence models, however, is that they require serialization of input ... See full document
11
AMR Parsing as Sequence to Graph Transduction
... Attention-based parsing with Seq2Seq-style models have been considered (Barzdins and Gosko, 2016; Peng et ...standard AMR training ...labeled AMR data, with no data augmentation. 6 AMR ... See full document
15
Still not there? Comparing Traditional Sequence to Sequence Models to Encoder Decoder Neural Networks on Monotone String Translation Tasks
... For lemmatization, we use the Wiktionary Morphology Dataset (Durrett and DeNero, 2013). The data set contains inflected forms for different languages and parts of speech, corresponding lemmas, and detailed inflection ... See full document
12
Self Attention Architectures for Answer Agnostic Neural Question Generation
... Neural sequence-to-sequence models often rely on Encoder-Decoder architectures: indeed, Recurrent Neural Networks (RNNs) have consistently pro- vided state-of-the-art results for ... See full document
6
A Detailed Evaluation of Neural Sequence to Sequence Models for In domain and Cross domain Text Simplification
... To better understand some phenomena noticed during the manual evaluation, which are specific for our NTS ap- proach and datasets, we count the number of sentence split- tings, sentence shortenings and wrong named entity ... See full document
8
Retrieve and Refine: Improved Sequence Generation Models For Dialogue
... The results are given in Table 1. They show that the RetNRef model can indeed improve perplexity with label neighbors or the label itself. However, surprisingly there is almost no difference between using no retrieval, ... See full document
6
Tutorial: De mystifying Neural MT
... Neural Statistical Machine Translation Neural Machine Translation Encoder Decoder Sequence-to-sequence learning: Encoder Sequence-to-sequence learning: Decoder Let’s use a simple NN for [r] ... See full document
84
Neural Sequence Labelling Models for Grammatical Error Correction
... a neural net- work joint model that has been adapted using native-language-specific learner text as a feature in SMT, while Chollampatt et ...a neural network global lexicon model and a neural ... See full document
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