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Found 26 papers, 7 papers with code
Representation of linguistic and domain knowledge for second language learning in virtual worlds
There has been much debate, both theoretical and practical, on how to link ontologies and lexicons in natural language processing (NLP) applications.
Building RDF Content for Data-to-Text Generation
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.
A Statistical, Grammar-Based Approach to Microplanning
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.
Analysing Data-To-Text Generation Benchmarks
Recently, several data-sets associating data to text have been created to train data-to-text surface realisers.
Creating Training Corpora for NLG Micro-Planners
In this paper, we present a novel framework for semi-automatically creating linguistically challenging micro-planning data-to-text corpora from existing Knowledge Bases.
The WebNLG Challenge: Generating Text from RDF Data
The WebNLG challenge consists in mapping sets of RDF triples to text.
Bootstrapping Generators from Noisy Data
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.
Deep Graph Convolutional Encoders for Structured Data to Text Generation
Most previous work on neural text generation from graph-structured data relies on standard sequence-to-sequence methods.
Ranked #2 on
Data-to-Text Generation
on SR11Deep
Generating Summaries with Topic Templates and Structured Convolutional Decoders
Existing neural generation approaches create multi-sentence text as a single sequence.
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics.
Ranked #1 on
Extreme Summarization
on GEM-XSum
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+5
Automatic Construction of Evaluation Suites for Natural Language Generation Datasets
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.
Multi-Document Summarization withDeterminantal Point Process Attention
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.
Ranked #3 on
Multi-Document Summarization
on Multi-News
Models and Datasets for Cross-Lingual Summarisation
We present a cross-lingual summarisation corpus with long documents in a source language associated with multi-sentence summaries in a target language.
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.
Semantic Parsing for Conversational Question Answering over Knowledge Graphs
In this paper, we are interested in developing semantic parsers which understand natural language questions embedded in a conversation with a user and ground them to formal queries over definitions in a general purpose knowledge graph (KG) with very large vocabularies (covering thousands of concept names and relations, and millions of entities).
Improving User Controlled Table-To-Text Generation Robustness
In this work we study user controlled table-to-text generation where users explore the content in a table by selecting cells and reading a natural language description thereof automatically produce by a natural language generator.
Fine-Grained Natural Language Inference Based Faithfulness Evaluation for Diverse Summarisation Tasks
We study existing approaches to leverage off-the-shelf Natural Language Inference (NLI) models for the evaluation of summary faithfulness and argue that these are sub-optimal due to the granularity level considered for premises and hypotheses.
The Hallucinations Leaderboard -- An Open Effort to Measure Hallucinations in Large Language Models
Large Language Models (LLMs) have transformed the Natural Language Processing (NLP) landscape with their remarkable ability to understand and generate human-like text.
