Neural Network Methods for Natural Language Processing

By Yoav Goldberg. 2017

323 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Hypothesis Only Baselines in Natural Language Inference (2018, neutral): “In the Story Cloze task (Mostafazadeh et al., 2016, 2017), a model is presented with a short four-sentence narrative and asked to complete it by choosing one of two suggested concluding sentences.”
How Do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking (2020, neutral): “The classiﬁer g is trained to mask as much of the input as possible without changing the output (minimizing a divergence D (cid:63) ). provements over traditional approaches on many tasks (Goldberg, 2017).”
Language Models for Code-switch Detection of te reo Māori and English in a Low-resource Setting (2022, background): “The Māori syllable structure consists of a nucleus, which may be occupied by a vowel (or a diphthong), and an optional onset (syllable start) occupied by a single consonant (consonant clusters are not present in Māori) (Harlow, 2007).”
Neural Machine Translation (2020, unknown): “There is also book on neural network methods applied to the natural language processing in general (Goldberg, 2017).”
Attention in Natural Language Processing (2019, unknown): “What this survey does not offer is a comprehensive account of all the neural architectures for NLP (for an excellent overview, see [131]) or of all the neural architectures for NLP that uses an attention mechanism.”
Measuring Gender Bias in Word Embeddings across Domains and Discovering New Gender Bias Word Categories (2019, background): “It is known that window size affects semantic similarity: larger window sizes tend to capture broader, more topical similarities between words whilst smaller windows capture more linguistic or even syntactic similarities (Goldberg, 2017, Sec.”
A survey of deep learning techniques for autonomous driving (2019, unknown): “) and Natural Language Processing (NLP) ( ?”
Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples (2017, unknown): “5 We refer the interested readers to textbooks such as [15,13] or to the documentation of the PyTorch framework, for the exact forms of the GRU and the LSTM.”
Neural language models as psycholinguistic subjects: Representations of syntactic state (2019, unknown): “It is now standard practice in NLP to derive sentence representations using neural sequence models of various kinds (Elman, 1990; Sutskever et al., 2014; Goldberg, 2017; Peters et al., 2018; De-vlin et al., 2018).”
Evaluating Text GANs as Language Models (2018, unknown): “, 2016) and many other applications (Goldberg, 2017).”
RNNs as psycholinguistic subjects: Syntactic state and grammatical dependency (2018, unknown): “Progress in natural language processing has recently come from deriving sentence representations using recurrent neural networks (RNNs) (Elman, 1990; Sutskever et al., 2014; Goldberg, 2017).”