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Found 15 papers, 5 papers with code
Language Modeling with Syntactic and Semantic Representation for Sentence Acceptability Predictions
Our experiments also show that neither syntactic nor semantic tags improve the performance of LSTM language models on the task of predicting sentence acceptability judgments.
A Neural Model for Compositional Word Embeddings and Sentence Processing
We propose a new neural model for word embeddings, which uses Unitary Matrices as the primary device for encoding lexical information.
Predicates as Boxes in Bayesian Semantics for Natural Language
In this way we construct a model, by specifying boxes for the predicates.
Assessing the Unitary RNN as an End-to-End Compositional Model of Syntax
We show that both an LSTM and a unitary-evolution recurrent neural network (URN) can achieve encouraging accuracy on two types of syntactic patterns: context-free long distance agreement, and mildly context-sensitive cross serial dependencies.
How Furiously Can Colourless Green Ideas Sleep? Sentence Acceptability in Context
We study the influence of context on sentence acceptability.
How Furiously Can Colorless Green Ideas Sleep? Sentence Acceptability in Context
We study the influence of context on sentence acceptability.
Bayesian Inference Semantics: A Modelling System and A Test Suite
We present BIS, a Bayesian Inference Semantics, for probabilistic reasoning in natural language.
The Effect of Context on Metaphor Paraphrase Aptness Judgments
We conduct two experiments to study the effect of context on metaphor paraphrase aptness judgments.
A Compositional Bayesian Semantics for Natural Language
We propose a compositional Bayesian semantics that interprets declarative sentences in a natural language by assigning them probability conditions.
The Influence of Context on Sentence Acceptability Judgements
We investigate the influence that document context exerts on human acceptability judgements for English sentences, via two sets of experiments.
Predicting Human Metaphor Paraphrase Judgments with Deep Neural Networks
We propose a new annotated corpus for metaphor interpretation by paraphrase, and a novel DNN model for performing this task.
