Sentence examples for sentence representation from inspiring English sources

The model makes a hard attention through the composition from sentence representation into document representation.

So first the model learns sentence representation where we can gain from both past and future input features thanks to Bidirectional Long Short-Term Memory Networks architecture.

The system takes as an input an under-specified abstract sentence representation (cf. Sect. 3.2).

The General-Purpose Sentence Representation Learning team will work to train deep learning models to understand sentence meaning.

The work in [2], for instance, considers its own sentence representation in the form of labelled directed graphs.

The top-level sentence representation is the sentence clause, in this case representing a fixed order Agent-Action-Patient-Punctuation sentence template.

We define sentence vector-based models as those which perform classification on the sole basis of a pair of fixed-size sentence representations that are computed independently of one another.

The resulting abstract sentence representations are similar to the previous Example 1 in Sect.

Surface realisation can be viewed as the task of mapping abstract sentence representations to word strings in a target language.

In natural language generation (NLG) systems, surface realisation is known as the task of mapping abstract sentence representations to a surface form, that is, a sequence of words, punctuation symbols etc., to be delivered to the document presentation system [1].

However, these models whether use many hand-crafted features case of machine learning methods or use sentence level representation processing a sequence without any correlations between labels in neighborhoods which have great influences on predicting the current label case of deep learning models.