Neural language model
A Comparison of Character Neural Language Model and Bootstrapping for Language Identification in Multilingual Noisy Texts
... pre-trained neural language model (LM), and data bootstrapping to overcome the problem of unbalanced limited ...of language identifica- tion in mixed-language short non-edited texts ...
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Topically Driven Neural Language Model
... a neural language model that incorporates document context in the form of a topic model-like architecture, thus providing a succinct representation of the broader document context outside of ...
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Feature based Neural Language Model and Chinese Word Segmentation
... of neural language model have been proved very useful in NLP ...of language modeling, part-of-speech tag- ging, chunking, named entity recognition, seman- tic role labeling and syntactic ...
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What a neural language model tells us about spatial relations
... generative neural language ...the language model is able to encode a distinction between functional and geometric bias of spatial relations and how the two dimensions of mean- ing ...a ...
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A Neural Language Model for Dynamically Representing the Meanings of Unknown Words and Entities in a Discourse
... novel neural language model that can build and dynamically change distributed representa- tions of words based on the multi-sentential dis- ...into neural networks is not ...a model ...
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MulCode: A Multiplicative Multi way Model for Compressing Neural Language Model
... Based on this assumption, (Chen et al., 2016) created a codebook by splitting the vocabulary into two disjoint sets based on the word frequency: the most frequent words and the rest. Less fre- quent words are represented ...
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Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Model
... LSTM 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 ...
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Affect LM: A Neural Language Model for Customizable Affective Text Generation
... spoken language, they have the following characteristics different from the Fisher corpus: (1) More emo- tional content as observed in Table 1, since they have been generated through a human subject’s spontaneous ...
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nlpUP at SemEval 2019 Task 6: A Deep Neural Language Model for Offensive Language Detection
... offensive language. Our pro- posed model, C-BiGRU, combines a Convolu- tional Neural Network (CNN) with a bidirec- tional Recurrent Neural Network ...our model is capable of detecting ...
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Syllable level Neural Language Model for Agglutinative Language
... Various language modeling methods that rely on character or morpheme like segmentation of words have been developed (Ciloglu et al., 2004; Cui et al., 2014; Kim et al., 2016; Mikolov et al., 2012; Zheng et al., ...
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Predicting Concrete and Abstract Entities in Modern Poetry
... a neural language model to select poetic entities, based on local context ...ing language models on the poetry of American modernists, both on seen and unseen poets, and across a range of ...
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CLUF: a Neural Model for Second Language Acquisition Modeling
... current Neural Networks (RNNs) to model stu- dent’s learning ...to model extremely long learning history that can range over months even ...psycholinguistic model of human memory with modern ...
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Automatically Generating Rhythmic Verse with Neural Networks
... a neural language model trained on a phonetic encoding to learn an implicit representation of both the form and content of English ...This model can effectively learn common poetic de- vices ...
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Enabling Real time Neural IME with Incremental Vocabulary Selection
... a neural language model tailored for IMEs to meet real-time infer- ence speed requirements on the conversion ...baseline model is composed of a LSTM- based language model ...
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Recurrent neural network language model adaptation for multi-genre broadcast speech recognition and alignment
... —Recurrent neural network language models (RNNLMs) generally outperform n -gram language models when used in automatic speech ...whilst model-based adaptation includes model fine-tuning ...
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MoL 2019 02: Neural language models with latent syntax
... proposal model in this we repeat the experiment 10 ...generative model increases with the number of samples, but clearly converges at around 100 samples; the difference between 10 and 100 samples is about 1 ...
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Language Modeling Through Neural Networks to Increase Performance of Speech Recognition System
... Language model estimates the probability distributions of various linguistic units or their ...that language models hardly take note of the fact that what is being modeled is ...
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Compound Probabilistic Context Free Grammars for Grammar Induction
... formation followed by a sigmoid. If SHIFT is cho- sen, we obtain a distribution over the vocabulary via another affine transformation over the stack representation followed by a softmax. Then we sample the next word from ...
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Unsupervised Latent Tree Induction with Deep Inside Outside Recursive Auto Encoders
... To test the impact of our modeling choices, we compared the performance of two different losses and four different composition functions on the full WSJ validation set. The losses were covered in Equations 1 (Margin) and ...
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Synthetic Literature: Writing Science Fiction in a Co Creative Process
... Natural Language Generation within a co- creative process, and examine where the co- creative setting challenges both writer and ma- ...character-level language model to generate text based on a ...
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