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Found 32 papers, 10 papers with code
DaMata: A Robot-Journalist Covering the Brazilian Amazon Deforestation
This demo paper introduces DaMata, a robot-journalist covering deforestation in the Brazilian Amazon.
aiXplain at Arabic Hate Speech 2022: An Ensemble Based Approach to Detecting Offensive Tweets
Abusive speech on online platforms has a detrimental effect on users’ mental health.
Generating Questions from Wikidata Triples
Question generation from knowledge bases (or knowledge base question generation, KBQG) is the task of generating questions from structured database information, typically in the form of triples representing facts.
The Third Multilingual Surface Realisation Shared Task (SR’20): Overview and Evaluation Results
As in SR’18 and SR’19, the shared task comprised two tracks: (1) a Shallow Track where the inputs were full UD structures with word order information removed and tokens lemmatised; and (2) a Deep Track where additionally, functional words and morphological information were removed.
The 2020 Bilingual, Bi-Directional WebNLG+ Shared Task: Overview and Evaluation Results (WebNLG+ 2020)
WebNLG+ offers two challenges: (i) mapping sets of RDF triples to English or Russian text (generation) and (ii) converting English or Russian text to sets of RDF triples (semantic parsing).
Another PASS: A Reproduction Study of the Human Evaluation of a Football Report Generation System
Clarity had a higher degree of reproducibility than Fluency, as measured by the coefficient of variation.
MTLens: Machine Translation Output Debugging
The performance of Machine Translation (MT) systems varies significantly with inputs of diverging features such as topics, genres, and surface properties.
Enriching the E2E dataset
This study introduces an enriched version of the E2E dataset, one of the most popular language resources for data-to-text NLG.
Evaluating Recognizing Question Entailment Methods for a Portuguese Community Question-Answering System about Diabetes Mellitus
This study describes the development of a Portuguese Community-Question Answering benchmark in the domain of Diabetes Mellitus using a Recognizing Question Entailment (RQE) approach.
Comparing Supervised Machine Learning Techniques for Genre Analysis in Software Engineering Research Articles
Written communication is of utmost importance to the progress of scientific research.
A Systematic Review of Data-to-Text NLG
Relevant literature in this field on datasets, evaluation metrics, application areas, multilingualism, language models, and hallucination mitigation methods is reviewed.
Word-Level ASR Quality Estimation for Efficient Corpus Sampling and Post-Editing through Analyzing Attentions of a Reference-Free Metric
The findings suggest that NoRefER is not merely a tool for error detection but also a comprehensive framework for enhancing ASR systems' transparency, efficiency, and effectiveness.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model
In terms of output quality, extending the training set of a data-to-text system with a language model using the pseudo-labeling approach did increase text quality scores, but the data augmentation approach yielded similar scores to the system without training set extension.
Building The First English-Brazilian Portuguese Corpus for Automatic Post-Editing
This paper introduces the first corpus for Automatic Post-Editing of English and a low-resource language, Brazilian Portuguese.
Referring to what you know and do not know: Making Referring Expression Generation Models Generalize To Unseen Entities
Data-to-text Natural Language Generation (NLG) is the computational process of generating natural language in the form of text or voice from non-linguistic data.
NABU $\mathrm{-}$ Multilingual Graph-based Neural RDF Verbalizer
The RDF-to-text task has recently gained substantial attention due to continuous growth of Linked Data.
Surface Realization Shared Task 2019 (MSR19): The Team 6 Approach
This study describes the approach developed by the Tilburg University team to the shallow track of the Multilingual Surface Realization Shared Task 2019 (SR{'}19) (Mille et al., 2019).
Question Similarity in Community Question Answering: A Systematic Exploration of Preprocessing Methods and Models
Community Question Answering forums are popular among Internet users, and a basic problem they encounter is trying to find out if their question has already been posed before.
Neural data-to-text generation: A comparison between pipeline and end-to-end architectures
In contrast, recent neural models for data-to-text generation have been proposed as end-to-end approaches, where the non-linguistic input is rendered in natural language with much less explicit intermediate representations in-between.
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Enriching the WebNLG corpus
This paper describes the enrichment of WebNLG corpus (Gardent et al., 2017a, b), with the aim to further extend its usefulness as a resource for evaluating common NLG tasks, including Discourse Ordering, Lexicalization and Referring Expression Generation.
Surface Realization Shared Task 2018 (SR18): The Tilburg University Approach
This study describes the approach developed by the Tilburg University team to the shallow task of the Multilingual Surface Realization Shared Task 2018 (SR18).
NeuralREG: An end-to-end approach to referring expression generation
Traditionally, Referring Expression Generation (REG) models first decide on the form and then on the content of references to discourse entities in text, typically relying on features such as salience and grammatical function.
RDF2PT: Generating Brazilian Portuguese Texts from RDF Data
The generation of natural language from Resource Description Framework (RDF) data has recently gained significant attention due to the continuous growth of Linked Data.
Improving the generation of personalised descriptions
Referring expression generation (REG) models that use speaker-dependent information require a considerable amount of training data produced by every individual speaker, or may otherwise perform poorly.
Linguistic realisation as machine translation: Comparing different MT models for AMR-to-text generation
In this paper, we study AMR-to-text generation, framing it as a translation task and comparing two different MT approaches (Phrase-based and Neural MT).
Trainable Referring Expression Generation using Overspecification Preferences
Referring expression generation (REG) models that use speaker-dependent information require a considerable amount of training data produced by every individual speaker, or may otherwise perform poorly.
Generating flexible proper name references in text: Data, models and evaluation
The model relies on the REGnames corpus, a dataset with 53, 102 proper name references to 1, 000 people in different discourse contexts.
