Search Results for author: Laura Perez-Beltrachini

Found 21 papers, 4 papers with code

Models and Datasets for Cross-Lingual Summarisation

1 code implementation EMNLP 2021 Laura Perez-Beltrachini, Mirella Lapata

We present a cross-lingual summarisation corpus with long documents in a source language associated with multi-sentence summaries in a target language.

Multi-Document Summarization withDeterminantal Point Process Attention

no code implementations Journal of Artificial Intelligence Research 2021 Laura Perez-Beltrachini, Mirella Lapata

The ability to convey relevant and diverse information is critical in multi-documentsummarization and yet remains elusive for neural seq-to-seq models whose outputs are of-ten redundant and fail to correctly cover important details.

Document Summarization Multi-Document Summarization

Automatic Construction of Evaluation Suites for Natural Language Generation Datasets

no code implementations16 Jun 2021 Simon Mille, Kaustubh D. Dhole, Saad Mahamood, Laura Perez-Beltrachini, Varun Gangal, Mihir Kale, Emiel van Miltenburg, Sebastian Gehrmann

By applying this framework to the GEM generation benchmark, we propose an evaluation suite made of 80 challenge sets, demonstrate the kinds of analyses that it enables and shed light onto the limits of current generation models.

Text Generation

Bootstrapping Generators from Noisy Data

1 code implementation NAACL 2018 Laura Perez-Beltrachini, Mirella Lapata

A core step in statistical data-to-text generation concerns learning correspondences between structured data representations (e. g., facts in a database) and associated texts.

Data-to-Text Generation

Creating Training Corpora for NLG Micro-Planners

no code implementations ACL 2017 Claire Gardent, Anastasia Shimorina, Shashi Narayan, Laura Perez-Beltrachini

In this paper, we present a novel framework for semi-automatically creating linguistically challenging micro-planning data-to-text corpora from existing Knowledge Bases.

Data-to-Text Generation Referring Expression +2

Analysing Data-To-Text Generation Benchmarks

no code implementations WS 2017 Laura Perez-Beltrachini, Claire Gardent

Recently, several data-sets associating data to text have been created to train data-to-text surface realisers.

Data-to-Text Generation

A Statistical, Grammar-Based Approach to Microplanning

no code implementations CL 2017 Claire Gardent, Laura Perez-Beltrachini

Although there has been much work in recent years on data-driven natural language generation, little attention has been paid to the fine-grained interactions that arise during microplanning between aggregation, surface realization, and sentence segmentation.

Sentence segmentation Text Generation

Building RDF Content for Data-to-Text Generation

no code implementations COLING 2016 Laura Perez-Beltrachini, Rania Sayed, Claire Gardent

In Natural Language Generation (NLG), one important limitation is the lack of common benchmarks on which to train, evaluate and compare data-to-text generators.

Data-to-Text Generation

Representation of linguistic and domain knowledge for second language learning in virtual worlds

no code implementations LREC 2012 Alex Denis, re, Ingrid Falk, Claire Gardent, Laura Perez-Beltrachini

There has been much debate, both theoretical and practical, on how to link ontologies and lexicons in natural language processing (NLP) applications.

Text Generation

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