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Open AccessProceedings ArticleDOI

The WebNLG Challenge: Generating Text from RDF Data

TLDR
The microplanning task is introduced, data preparation, evaluation methodology, participant results and a brief description of the participating systems are provided.
Abstract
The WebNLG challenge consists in mapping sets of RDF triples to text It provides a common benchmark on which to train, evaluate and compare “microplanners”, ie generation systems that verbalise a given content by making a range of complex interacting choices including referring expression generation, aggregation, lexicalisation, surface realisation and sentence segmentation In this paper, we introduce the microplanning task, describe data preparation, introduce our evaluation methodology, analyse participant results and provide a brief description of the participating systems

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Journal ArticleDOI

Knowledge Graphs

TL;DR: The historical events that lead to the interweaving of data and knowledge are tracked to help improve knowledge and understanding of the world around us.
Book Chapter

Language and thought

D Laplane
TL;DR: This article showed that the meaning of a word depends on a context of signification which may or may not be supplied verbally and that the initial progress made by children is not related to language but to brain maturation.
Posted Content

BLEURT: Learning Robust Metrics for Text Generation

TL;DR: BLEURT, a learned evaluation metric for English based on BERT, can model human judgment with a few thousand possibly biased training examples and yields superior results even when the training data is scarce and out-of-distribution.
Proceedings ArticleDOI

BLEURT: Learning Robust Metrics for Text Generation

TL;DR: This paper proposed BLEURT, a learned evaluation metric for English based on BERT, which can model human judgment with a few thousand possibly biased training examples and achieved state-of-the-art results on the last three years of the WMT Metrics shared task and the WebNLG data set.
Posted Content

ToTTo: A Controlled Table-To-Text Generation Dataset

TL;DR: An open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description.
References
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Proceedings ArticleDOI

Bleu: a Method for Automatic Evaluation of Machine Translation

TL;DR: This paper proposed a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run.
Proceedings Article

Neural Machine Translation by Jointly Learning to Align and Translate

TL;DR: It is conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and it is proposed to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.
Posted Content

Neural Machine Translation by Jointly Learning to Align and Translate

TL;DR: In this paper, the authors propose to use a soft-searching model to find the parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.
Proceedings ArticleDOI

Moses: Open Source Toolkit for Statistical Machine Translation

TL;DR: An open-source toolkit for statistical machine translation whose novel contributions are support for linguistically motivated factors, confusion network decoding, and efficient data formats for translation models and language models.
Proceedings Article

A Study of Translation Edit Rate with Targeted Human Annotation

TL;DR: A new, intuitive measure for evaluating machine translation output that avoids the knowledge intensiveness of more meaning-based approaches, and the labor-intensiveness of human judgments is defined.