[PDF] Top 20 A Neural Attention Model for Disfluency Detection
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A Neural Attention Model for Disfluency Detection
... and disfluency annotations are ...hinders disfluency detection’s ...recurrent neural network (RNN), which can capture dependencies at any length in theory, on disfluency detection ... See full document
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Online abuse detection: the value of preprocessing and neural attention models
... Recurrent Neural Network ...Convolutional Neural Networks (CNN) on the same ...HybridCNN model, which is a variant of CNN that uses both words and characters to make classification ... See full document
9
Tweet Stance Detection Using an Attention based Neural Ensemble Model
... proposed neural ensemble model (PNEM) during ...embedding model pre- trained on Wikipedia with skip-gram (Bojanowski et ...our model, DC-Bi-LSTM module contains 5 layers and NL- STMs module ... See full document
6
Parsing Speech: a Neural Approach to Integrating Lexical and Acoustic Prosodic Information
... ency detection via parse ...separate disfluency detection mod- ule; we hypothesized that the location-sensitive at- tention model helps handle these differences based on analysis of the ... See full document
13
Transition Based Disfluency Detection using LSTMs
... take disfluency detection as a sequence labeling problem, where each senten- tial word is assigned with a label (Georgila, 2009; Qian and Liu, ...recurrent neural network (RNN), which can capture ... See full document
10
Adapting Translation Models for Transcript Disfluency Detection
... Transcript disfluency detection (TDD) is an important com- ponent of the real-time speech translation system, which arouses more and more interests in recent ...adapting neural machine transla- tion ... See full document
8
Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Model
... guage model is already a state-of-the-art system, providing a very strong baseline for our ...both model-based scores (in- cluding NCM scores and LM probabilities) and surface pattern features (which are ... See full document
7
Joint, Incremental Disfluency Detection and Utterance Segmentation from Speech
... Disfluency detection on pre-segmented utter- ances in the Switchboard corpus has also had a lot of attention, and has also reached high per- formance (Johnson and Charniak, 2004; Georgila, 2009; Qian ... See full document
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Giving Attention to the Unexpected: Using Prosody Innovations in Disfluency Detection
... found between text and prosody. This difference can be measured using a z-score, which is a mea- sure of how many standard deviations below or above the population mean an observation is. This framework can be viewed as ... See full document
10
Disfluency Detection using Auto Correlational Neural Networks
... to disfluency detection in sponta- neous speech transcripts currently still de- pend on an array of hand-crafted features, and other representations derived from the output of pre-existing systems such as ... See full document
10
Hindi-English Neural Machine Translation Using Attention Model
... how attention based techniques are improving the quality of Neural Machine Translation (NMT) ...encoder-decoder model used in NMT ...in model coverage of an NMT ... See full document
5
Disfluency Detection Using Multi step Stacked Learning
... channel model which showed great im- provement over boosting based classifier (Charniak and Johnson, ...this model using minimal expected F-loss oriented n-best ... See full document
6
Look Harder: A Neural Machine Translation Model with Hard Attention
... Transformer model and the original soft-attention based Transformer model indicates the effectiveness of selecting a few rele- vant source tokens for each target ...self- attention layer to ... See full document
7
Summarizing Source Code using a Neural Attention Model
... Finally, we performed manual error analysis on 50 randomly selected examples from DEV (Ta- ble 7) for each language. Redundancy is a ma- jor source of error, i.e., generation of extraneous content-bearing phrases, along ... See full document
11
A Nested Attention Neural Hybrid Model for Grammatical Error Correction
... nested attention on the GEC task. As seen, our nested attention hybrid model substan- tially improves upon the baseline, achieving a gain of close to 3 points on the test ...word/character ... See full document
10
Detecting Adverse Drug Reactions from Biomedical Texts with Neural Networks
... deep neural network models for entity-level ADR classification (Alimova and Tu- tubalina, ...Interactive Attention Neural network (IAN) (Ma et ...following attention-based neu- ral networks ... See full document
7
The impact of language models and loss functions on repair disfluency detection
... Snover et al. (2004) investigate the use of purely lexical features combined with part-of-speech tags to detect disfluencies. This approach is compared to approaches which use primarily prosodic cues, and appears to ... See full document
9
A Cognition Based Attention Model for Sentiment Analysis
... Table 5 shows that among all three single atten- tion models, UPA outperforms both LA and CBA in the first three datasets. This is easier to under- stand as UPA already included LA and it has more explicit information ... See full document
10
Joint Parsing and Disfluency Detection in Linear Time
... Parser Evaluation We evaluate our parser with both unlabeled attachment accuracy of correct words and precision and recall of finding the dependencies of correct words. 7 The second classifier is trained with 3 ... See full document
6
A Visual Attention Grounding Neural Model for Multimodal Machine Translation
... translation model and (2) con- structing a visual-language shared embedding that grounds the visual semantics with ...VAG-NMT model. We adopt a state-of-the-art attention-based sequence- to-sequence ... See full document
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