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Found 11 papers, 5 papers with code
Storytelling from Structured Data and Knowledge Graphs : An NLG Perspective
In this tutorial, we wish to cover the foundational, methodological, and system development aspects of translating structured data (such as data in tabular form) and knowledge bases (such as knowledge graphs) into natural language.
On Controllable Sparse Alternatives to Softmax
Converting an n-dimensional vector to a probability distribution over n objects is a commonly used component in many machine learning tasks like multiclass classification, multilabel classification, attention mechanisms etc.
Unsupervised Neural Text Simplification
The paper presents a first attempt towards unsupervised neural text simplification that relies only on unlabeled text corpora.
Ranked #2 on
Text Simplification
on ASSET
Scalable Micro-planned Generation of Discourse from Structured Data
We present a framework for generating natural language description from structured data such as tables; the problem comes under the category of data-to-text natural language generation (NLG).
A Mixed Hierarchical Attention based Encoder-Decoder Approach for Standard Table Summarization
Structured data summarization involves generation of natural language summaries from structured input data.
Generating Descriptions from Structured Data Using a Bifocal Attention Mechanism and Gated Orthogonalization
For example, while generating descriptions from a table, a human would attend to information at two levels: (i) the fields (macro level) and (ii) the values within the field (micro level).
A Machine Learning Approach for Evaluating Creative Artifacts
Our proposed learning framework is applicable to all creative domains; yet we evaluate it on a dataset of movies created from IMDb and Rotten Tomatoes due to availability of audience and critic scores, which can be used as proxy ground truth labels for creativity.
Story Generation from Sequence of Independent Short Descriptions
Existing Natural Language Generation (NLG) systems are weak AI systems and exhibit limited capabilities when language generation tasks demand higher levels of creativity, originality and brevity.
Diversity driven Attention Model for Query-based Abstractive Summarization
Abstractive summarization aims to generate a shorter version of the document covering all the salient points in a compact and coherent fashion.
Ranked #2 on
Query-Based Extractive Summarization
on Debatepedia
Joint Learning of Correlated Sequence Labelling Tasks Using Bidirectional Recurrent Neural Networks
The stream of words produced by Automatic Speech Recognition (ASR) systems is typically devoid of punctuations and formatting.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
An Empirical Evaluation of various Deep Learning Architectures for Bi-Sequence Classification Tasks
Several tasks in argumentation mining and debating, question-answering, and natural language inference involve classifying a sequence in the context of another sequence (referred as bi-sequence classification).
