Text Generation with Text-Editing Models

• URL: http://arxiv.org/abs/2206.07043v1
• Date: Tue, 14 Jun 2022 17:58:17 GMT
• Title: Text Generation with Text-Editing Models
• Authors: Eric Malmi, Yue Dong, Jonathan Mallinson, Aleksandr Chuklin, Jakub Adamek, Daniil Mirylenka, Felix Stahlberg, Sebastian Krause, Shankar Kumar, Aliaksei Severyn
• Abstract summary: This tutorial provides a comprehensive overview of text-editing models and current state-of-the-art approaches. We discuss challenges related to productionization and how these models can be used to mitigate hallucination and bias.
• Score: 78.03750739936956
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-editing models have recently become a prominent alternative to seq2seq models for monolingual text-generation tasks such as grammatical error correction, simplification, and style transfer. These tasks share a common trait - they exhibit a large amount of textual overlap between the source and target texts. Text-editing models take advantage of this observation and learn to generate the output by predicting edit operations applied to the source sequence. In contrast, seq2seq models generate outputs word-by-word from scratch thus making them slow at inference time. Text-editing models provide several benefits over seq2seq models including faster inference speed, higher sample efficiency, and better control and interpretability of the outputs. This tutorial provides a comprehensive overview of text-editing models and current state-of-the-art approaches, and analyzes their pros and cons. We discuss challenges related to productionization and how these models can be used to mitigate hallucination and bias, both pressing challenges in the field of text generation.

Related papers

• Contextualized Diffusion Models for Text-Guided Image and Video Generation [67.69171154637172]
  Conditional diffusion models have exhibited superior performance in high-fidelity text-guided visual generation and editing. We propose a novel and general contextualized diffusion model (ContextDiff) by incorporating the cross-modal context encompassing interactions and alignments between text condition and visual sample. We generalize our model to both DDPMs and DDIMs with theoretical derivations, and demonstrate the effectiveness of our model in evaluations with two challenging tasks: text-to-image generation, and text-to-video editing.
  arXiv  Detail & Related papers  (2024-02-26T15:01:16Z)
• PLANNER: Generating Diversified Paragraph via Latent Language Diffusion Model [37.2192243883707]
  We propose PLANNER, a model that combines latent semantic diffusion with autoregressive generation to generate fluent text. Results on semantic generation, text completion and summarization show its effectiveness in generating high-quality long-form text.
  arXiv  Detail & Related papers  (2023-06-05T01:36:39Z)
• SeqDiffuSeq: Text Diffusion with Encoder-Decoder Transformers [50.90457644954857]
  In this work, we apply diffusion models to approach sequence-to-sequence text generation. We propose SeqDiffuSeq, a text diffusion model for sequence-to-sequence generation. Experiment results illustrate the good performance on sequence-to-sequence generation in terms of text quality and inference time.
  arXiv  Detail & Related papers  (2022-12-20T15:16:24Z)
• eDiffi: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers [87.52504764677226]
  Large-scale diffusion-based generative models have led to breakthroughs in text-conditioned high-resolution image synthesis. We train an ensemble of text-to-image diffusion models specialized for different stages synthesis. Our ensemble of diffusion models, called eDiffi, results in improved text alignment while maintaining the same inference cost.
  arXiv  Detail & Related papers  (2022-11-02T17:43:04Z)
• DiffusER: Discrete Diffusion via Edit-based Reconstruction [88.62707047517914]
  DiffusER is an edit-based generative model for text based on denoising diffusion models. It can rival autoregressive models on several tasks spanning machine translation, summarization, and style transfer. It can also perform other varieties of generation that standard autoregressive models are not well-suited for.
  arXiv  Detail & Related papers  (2022-10-30T16:55:23Z)
• Text Revision by On-the-Fly Representation Optimization [76.11035270753757]
  Current state-of-the-art methods formulate these tasks as sequence-to-sequence learning problems. We present an iterative in-place editing approach for text revision, which requires no parallel data. It achieves competitive and even better performance than state-of-the-art supervised methods on text simplification.
  arXiv  Detail & Related papers  (2022-04-15T07:38:08Z)
• Data-to-Text Generation with Iterative Text Editing [3.42658286826597]
  We present a novel approach to data-to-text generation based on iterative text editing. We first transform data items to text using trivial templates, and then we iteratively improve the resulting text by a neural model trained for the sentence fusion task. The output of the model is filtered by a simple and reranked with an off-the-shelf pre-trained language model.
  arXiv  Detail & Related papers  (2020-11-03T13:32:38Z)
• Felix: Flexible Text Editing Through Tagging and Insertion [21.55417495142206]
  Felix is a flexible text-editing approach for generation, designed to derive the maximum benefit from the ideas of decoding with bi-directional contexts and self-supervised pre-training. We achieve this by decomposing the text-editing task into two sub-tasks: tagging to decide on the subset of input tokens and their order in the output text and insertion to in-fill the missing tokens in the output not present in the input.
  arXiv  Detail & Related papers  (2020-03-24T07:01:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.