Papers Data-to-Text Generation
“Data-to-Text Generation” 태그가 달린 논문 223편 · 필터 해제
Cross-Domain, Multi-Task Data-to-Text Generation without In-Domain Training Data
Structured data exists in many forms (tables, knowledge graphs, charts, and time series), and converting it into text may involve different generation tasks. However, most prior work on data-to-text (D2T) generation has …
Data-to-Text GenerationKnowledge DistillationKnowledge GraphsTailNLG: A Multilingual Benchmark Addressing Verbalization of Long-Tail Entities
The automatic verbalization of structured knowledge is a key task for making knowledge graphs accessible to non-expert users and supporting retrieval-augmented generation systems. Although recent advances in Data-to-Text…
Data-to-Text GenerationKnowledge GraphsMzansiText and MzansiLM: An Open Corpus and Decoder-Only Language Model for South African Languages
Decoder-only language models can be adapted to diverse tasks through instruction finetuning, but the extent to which this generalizes at small scale for low-resource languages remains unclear. We focus on the languages o…
Natural Language UnderstandingData-to-Text GenerationNews ClassificationTurk-LettuceDetect: A Hallucination Detection Models for Turkish RAG Applications
The widespread adoption of Large Language Models (LLMs) has been hindered by their tendency to hallucinate, generating plausible but factually incorrect information. While Retrieval-Augmented Generation (RAG) systems att…
Computational EfficiencyData-to-Text GenerationQuestion AnsweringLarge Language Models as Span Annotators
For high-quality texts, single-score metrics seldom provide actionable feedback. In contrast, span annotation - pointing out issues in the text by annotating their spans - can guide improvements and provide insights. Unt…
Data-to-Text GenerationMachine TranslationPropaganda detectionText Generation+1SCOPE: A Self-supervised Framework for Improving Faithfulness in Conditional Text Generation
Large Language Models (LLMs), when used for conditional text generation, often produce hallucinations, i.e., information that is unfaithful or not grounded in the input context. This issue arises in typical conditional t…
Conditional Text GenerationData-to-Text GenerationText GenerationText SummarizationEvaluation of NMT-Assisted Grammar Transfer for a Multi-Language Configurable Data-to-Text System
One approach for multilingual data-to-text generation is to translate grammatical configurations upfront from the source language into each target language. These configurations are then used by a surface realizer and in…
Data-to-Text GenerationMachine TranslationNMTText Generation+1Curriculum Learning for Cross-Lingual Data-to-Text Generation With Noisy Data
Curriculum learning has been used to improve the quality of text generation systems by ordering the training samples according to a particular schedule in various tasks. In the context of data-to-text generation (DTG), p…
Data-to-Text GenerationText GenerationAn Extensive Evaluation of Factual Consistency in Large Language Models for Data-to-Text Generation
Large Language Models (LLMs) have shown exceptional performance across various Data-to-Text Generation (DTG) tasks. However, generating factually consistent text in DTG remains challenging for LLMs. Despite this, in-dept…
Data-to-Text GenerationText GenerationOntology-Free General-Domain Knowledge Graph-to-Text Generation Dataset Synthesis using Large Language Model
Knowledge Graph-to-Text (G2T) generation involves verbalizing structured knowledge graphs into natural language text. Recent advancements in Pretrained Language Models (PLMs) have improved G2T performance, but their effe…
Data-to-Text GenerationGraph-to-SequenceKnowledge GraphsLanguage Modeling+3Impact of Model Size on Fine-tuned LLM Performance in Data-to-Text Generation: A State-of-the-Art Investigation
Data-to-text (D2T) generation aims to generate human-readable text from semi-structured data, such as tables and graphs. The recent success of D2T is largely attributed to advancements in LLMs. Despite the success of LLM…
Data-to-Text GenerationInformativenessText GenerationModeling Comparative Logical Relation with Contrastive Learning for Text Generation
Data-to-Text Generation (D2T), a classic natural language generation problem, aims at producing fluent descriptions for structured input data, such as a table. Existing D2T works mainly focus on describing the superficia…
Contrastive LearningData-to-Text GenerationDecoderRelation+1SPOR: A Comprehensive and Practical Evaluation Method for Compositional Generalization in Data-to-Text Generation
Compositional generalization is an important ability of language models and has many different manifestations. For data-to-text generation, previous research on this ability is limited to a single manifestation called Sy…
Data-to-Text GenerationText GenerationBridging the Gap between Different Vocabularies for LLM Ensemble
Ensembling different large language models (LLMs) to unleash their complementary potential and harness their individual strengths is highly valuable. Nevertheless, vocabulary discrepancies among various LLMs have constra…
Arithmetic ReasoningData-to-Text GenerationMachine TranslationText GenerationPrompting for Numerical Sequences: A Case Study on Market Comment Generation
Large language models (LLMs) have been applied to a wide range of data-to-text generation tasks, including tables, graphs, and time-series numerical data-to-text settings. While research on generating prompts for structu…
Comment GenerationData-to-Text GenerationText GenerationTime SeriesTriples-to-isiXhosa (T2X): Addressing the Challenges of Low-Resource Agglutinative Data-to-Text Generation
Most data-to-text datasets are for English, so the difficulties of modelling data-to-text for low-resource languages are largely unexplored. In this paper we tackle data-to-text for isiXhosa, which is low-resource and ag…
Data-to-Text GenerationMachine TranslationText GenerationHigh-quality Data-to-Text Generation for Severely Under-Resourced Languages with Out-of-the-box Large Language Models
The performance of NLP methods for severely under-resourced languages cannot currently hope to match the state of the art in NLP methods for well resourced languages. We explore the extent to which pretrained large langu…
Data-to-Text GenerationText GenerationA Systematic Review of Data-to-Text NLG
This systematic review undertakes a comprehensive analysis of current research on data-to-text generation, identifying gaps, challenges, and future directions within the field. Relevant literature in this field on datase…
Data-to-Text GenerationHallucinationRe-RankingText GenerationTrICy: Trigger-guided Data-to-text Generation with Intent aware Attention-Copy
Data-to-text (D2T) generation is a crucial task in many natural language understanding (NLU) applications and forms the foundation of task-oriented dialog systems. In the context of conversational AI solutions that can w…
Data-to-Text GenerationNatural Language UnderstandingText GenerationSelf-training from Self-memory in Data-to-text Generation
This paper introduces a novel training model, self-training from self-memory (STSM) in data-to-text generation (DTG), allowing the model to self-train on subsets, including self-memory as outputs inferred directly from t…
Continual LearningData-to-Text GenerationText Generation