Papers Conditional Text Generation
“Conditional Text Generation” 태그가 달린 논문 71편 · 필터 해제
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset
We present a new dataset of Wikipedia articles each paired with a knowledge graph, to facilitate the research in conditional text generation, graph generation and graph representation learning. Existing graph-text paired…
ArticlesConditional Text GenerationGraph GenerationGraph Neural Network+6Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation
Prior studies on text-to-text generation typically assume that the model could figure out what to attend to in the input and what to include in the output via seq2seq learning, with only the parallel training data and no…
Conditional Text GenerationDenoisingText GenerationIs Everything in Order? A Simple Way to Order Sentences
The task of organizing a shuffled set of sentences into a coherent text has been used to evaluate a machine's understanding of causal and temporal relations. We formulate the sentence ordering task as a conditional text-…
Conditional Text GenerationSentenceSentence OrderingText GenerationUnifying Vision-and-Language Tasks via Text Generation
Existing methods for vision-and-language learning typically require designing task-specific architectures and objectives for each task. For example, a multi-label answer classifier for visual question answering, a region…
Conditional Text GenerationDecoderImage CaptioningLanguage Modeling+9Data-to-text Generation by Splicing Together Nearest Neighbors
We propose to tackle data-to-text generation tasks by directly splicing together retrieved segments of text from "neighbor" source-target pairs. Unlike recent work that conditions on retrieved neighbors but generates tex…
Conditional Text GenerationData-to-Text GenerationText GenerationLexically-constrained Text Generation through Commonsense Knowledge Extraction and Injection
Conditional text generation has been a challenging task that is yet to see human-level performance from state-of-the-art models. In this work, we specifically focus on the Commongen benchmark, wherein the aim is to gener…
Common Sense ReasoningConditional Text GenerationKnowledge GraphsLanguage Modeling+3Contrastive Learning with Adversarial Perturbations for Conditional Text Generation
Recently, sequence-to-sequence (seq2seq) models with the Transformer architecture have achieved remarkable performance on various conditional text generation tasks, such as machine translation. However, most of them are …
Conditional Text GenerationContrastive LearningMachine TranslationQuestion Generation+4NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints
Conditional text generation often requires lexical constraints, i.e., which words should or shouldn't be included in the output text. While the dominant recipe for conditional text generation has been large-scale pretrai…
Conditional Text GenerationText GenerationReflective Decoding: Beyond Unidirectional Generation with Off-the-Shelf Language Models
Publicly available, large pretrained LanguageModels (LMs) generate text with remarkable quality, but only sequentially from left to right. As a result, they are not immediately applicable to generation tasks that break t…
Conditional Text GenerationSentenceText GenerationText InfillingOn Long-Tailed Phenomena in Neural Machine Translation
State-of-the-art Neural Machine Translation (NMT) models struggle with generating low-frequency tokens, tackling which remains a major challenge. The analysis of long-tailed phenomena in the context of structured predict…
Conditional Text GenerationMachine TranslationNMTStructured Prediction+4Plug and Play Autoencoders for Conditional Text Generation
Text autoencoders are commonly used for conditional generation tasks such as style transfer. We propose methods which are plug and play, where any pretrained autoencoder can be used, and only require learning a mapping w…
Conditional Text GenerationNavigateStyle TransferText GenerationETC-NLG: End-to-end Topic-Conditioned Natural Language Generation
Plug-and-play language models (PPLMs) enable topic-conditioned natural language generation by pairing large pre-trained generators with attribute models used to steer the predicted token distribution towards the selected…
AttributeComputational EfficiencyConditional Text GenerationText Generation+1Hiring Now: A Skill-Aware Multi-Attention Model for Job Posting Generation
Writing a good job posting is a critical step in the recruiting process, but the task is often more difficult than many people think. It is challenging to specify the level of education, experience, relevant skills per t…
Conditional Text GenerationDecoderText GenerationTechnical Report: Auxiliary Tuning and its Application to Conditional Text Generation
We introduce a simple and efficient method, called Auxiliary Tuning, for adapting a pre-trained Language Model to a novel task; we demonstrate this approach on the task of conditional text generation. Our approach supple…
Conditional Text GenerationLanguage ModelingLanguage ModellingText GenerationImproving Disentangled Text Representation Learning with Information-Theoretic Guidance
Learning disentangled representations of natural language is essential for many NLP tasks, e.g., conditional text generation, style transfer, personalized dialogue systems, etc. Similar problems have been studied extensi…
Conditional Text GenerationRepresentation LearningStyle TransferText Generation+1Learning Implicit Text Generation via Feature Matching
Generative feature matching network (GFMN) is an approach for training implicit generative models for images by performing moment matching on features from pre-trained neural networks. In this paper, we present new GFMN …
Conditional Text GenerationStyle TransferText GenerationText Style Transfer+1Token Manipulation Generative Adversarial Network for Text Generation
MaskGAN opens the query for the conditional language model by filling in the blanks between the given tokens. In this paper, we focus on addressing the limitations caused by having to specify blanks to be filled. We deco…
Conditional Text GenerationDiversityGenerative Adversarial NetworkLanguage Modeling+2ToTTo: A Controlled Table-To-Text Generation Dataset
We present ToTTo, 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-s…
Conditional Text GenerationData-to-Text GenerationSentenceTable-to-Text Generation+1TextGAIL: Generative Adversarial Imitation Learning for Text Generation
Generative Adversarial Networks (GANs) for text generation have recently received many criticisms, as they perform worse than their MLE counterparts. We suspect previous text GANs' inferior performance is due to the lack…
Conditional Text GenerationDiversityImitation LearningText GenerationCG-BERT: Conditional Text Generation with BERT for Generalized Few-shot Intent Detection
In this paper, we formulate a more realistic and difficult problem setup for the intent detection task in natural language understanding, namely Generalized Few-Shot Intent Detection (GFSID). GFSID aims to discriminate a…
Conditional Text GenerationIntent DetectionLanguage ModelingLanguage Modelling+3