Языковые модели на основе нейронных сетей

method ID: neuralnetworklanguagemodels

Related Papers

Learning Sense-specific Word Embeddings By Exploiting Bilingual Resources

Authors: nan

Publication: TIPSTER (2014)

DOI: nan

Recent work has shown success in learning word embeddings with neural network language models (NNLM). However, the majority of previous NNLMs represent each word with a single embedding, which fails …...

5997

Higher-order Lexical Semantic Models for Non-factoid Answer Reranking

Authors: nan

Publication: TIPSTER (2015)

DOI: nan

AbstractLexical semantic models provide robust performance for question answering, but, in general, can only capitalize on direct evidence seen during training. For example, monolingual alignment models acquire term alignment probabilities …...

27176

Strategies for Training Large Vocabulary Neural Language Models

Authors: nan

Publication: TIPSTER (2016)

DOI: nan

Training neural network language mod- els over large vocabularies is computa- tionally costly compared to count-based models such as Kneser-Ney. We present a systematic comparison of neural strate- gies to …...

40307

Neural language models as psycholinguistic subjects: Representations of syntactic state

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractWe investigate the extent to which the behavior of neural network language models reflects incremental representations of syntactic state. To do so, we employ experimental methodologies which were originally developed …...

22280

Stolen Probability: A Structural Weakness of Neural Language Models

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractNeural Network Language Models (NNLMs) generate probability distributions by applying a softmax function to a distance metric formed by taking the dot product of a prediction vector with all word …...

38054