Neural Data-to-Text Generation with LM-based Text Augmentation

• URL: http://arxiv.org/abs/2102.03556v1
• Date: Sat, 6 Feb 2021 10:21:48 GMT
• Title: Neural Data-to-Text Generation with LM-based Text Augmentation
• Authors: Ernie Chang, Xiaoyu Shen, Dawei Zhu, Vera Demberg, Hui Su
• Abstract summary: We show that a weakly supervised training paradigm is able to outperform fully supervised seq2seq models with less than 10% annotations. By utilizing all annotated data, our model can boost the performance of a standard seq2seq model by over 5 BLEU points.
• Score: 27.822282190362856
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For many new application domains for data-to-text generation, the main obstacle in training neural models consists of a lack of training data. While usually large numbers of instances are available on the data side, often only very few text samples are available. To address this problem, we here propose a novel few-shot approach for this setting. Our approach automatically augments the data available for training by (i) generating new text samples based on replacing specific values by alternative ones from the same category, (ii) generating new text samples based on GPT-2, and (iii) proposing an automatic method for pairing the new text samples with data samples. As the text augmentation can introduce noise to the training data, we use cycle consistency as an objective, in order to make sure that a given data sample can be correctly reconstructed after having been formulated as text (and that text samples can be reconstructed from data). On both the E2E and WebNLG benchmarks, we show that this weakly supervised training paradigm is able to outperform fully supervised seq2seq models with less than 10% annotations. By utilizing all annotated data, our model can boost the performance of a standard seq2seq model by over 5 BLEU points, establishing a new state-of-the-art on both datasets.

Related papers

• Retrieval-Augmented Data Augmentation for Low-Resource Domain Tasks [66.87070857705994]
  In low-resource settings, the amount of seed data samples to use for data augmentation is very small. We propose a novel method that augments training data by incorporating a wealth of examples from other datasets. This approach can ensure that the generated data is not only relevant but also more diverse than what could be achieved using the limited seed data alone.
  arXiv  Detail & Related papers  (2024-02-21T02:45:46Z)
• Contrastive Transformer Learning with Proximity Data Generation for Text-Based Person Search [60.626459715780605]
  Given a descriptive text query, text-based person search aims to retrieve the best-matched target person from an image gallery. Such a cross-modal retrieval task is quite challenging due to significant modality gap, fine-grained differences and insufficiency of annotated data. In this paper, we propose a simple yet effective dual Transformer model for text-based person search.
  arXiv  Detail & Related papers  (2023-11-15T16:26:49Z)
• Faithful Low-Resource Data-to-Text Generation through Cycle Training [14.375070014155817]
  Methods to generate text from structured data have advanced significantly in recent years. Cycle training uses two models which are inverses of each other. We show that cycle training achieves nearly the same performance as fully supervised approaches.
  arXiv  Detail & Related papers  (2023-05-24T06:44:42Z)
• DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classification [56.817386699291305]
  This paper proposes a simple yet effective data augmentation approach termed DoubleMix. DoubleMix first generates several perturbed samples for each training data. It then uses the perturbed data and original data to carry out a two-step in the hidden space of neural models.
  arXiv  Detail & Related papers  (2022-09-12T15:01:04Z)
• A Data Cartography based MixUp for Pre-trained Language Models [47.90235939359225]
  MixUp is a data augmentation strategy where additional samples are generated during training by combining random pairs of training samples and their labels. We propose TDMixUp, a novel MixUp strategy that leverages Training Dynamics and allows more informative samples to be combined for generating new data samples. We empirically validate that our method not only achieves competitive performance using a smaller subset of the training data compared with strong baselines, but also yields lower expected calibration error on the pre-trained language model, BERT, on both in-domain and out-of-domain settings in a wide range of NLP tasks.
  arXiv  Detail & Related papers  (2022-05-06T17:59:19Z)
• A Benchmark Corpus for the Detection of Automatically Generated Text in Academic Publications [0.02578242050187029]
  This paper presents two datasets comprised of artificially generated research content. In the first case, the content is completely generated by the GPT-2 model after a short prompt extracted from original papers. The partial or hybrid dataset is created by replacing several sentences of abstracts with sentences that are generated by the Arxiv-NLP model. We evaluate the quality of the datasets comparing the generated texts to aligned original texts using fluency metrics such as BLEU and ROUGE.
  arXiv  Detail & Related papers  (2022-02-04T08:16:56Z)
• MeetSum: Transforming Meeting Transcript Summarization using Transformers! [2.1915057426589746]
  We utilize a Transformer-based Pointer Generator Network to generate abstract summaries for meeting transcripts. This model uses 2 LSTMs as an encoder and a decoder, a Pointer network which copies words from the inputted text, and a Generator network to produce out-of-vocabulary words. We show that training the model on a news summary dataset and using zero-shot learning to test it on the meeting dataset proves to produce better results than training it on the AMI meeting dataset.
  arXiv  Detail & Related papers  (2021-08-13T16:34:09Z)
• 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)
• KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation [100.79870384880333]
  We propose a knowledge-grounded pre-training (KGPT) to generate knowledge-enriched text. We adopt three settings, namely fully-supervised, zero-shot, few-shot to evaluate its effectiveness. Under zero-shot setting, our model achieves over 30 ROUGE-L on WebNLG while all other baselines fail.
  arXiv  Detail & Related papers  (2020-10-05T19:59:05Z)
• Partially-Aligned Data-to-Text Generation with Distant Supervision [69.15410325679635]
  We propose a new generation task called Partially-Aligned Data-to-Text Generation (PADTG) It is more practical since it utilizes automatically annotated data for training and thus considerably expands the application domains. Our framework outperforms all baseline models as well as verify the feasibility of utilizing partially-aligned data.
  arXiv  Detail & Related papers  (2020-10-03T03:18:52Z)
• Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity [3.8673630752805432]
  We present DataTuner, a neural, end-to-end data-to-text generation system. We take a two-stage generation-reranking approach, combining a fine-tuned language model with a semantic fidelity. We show that DataTuner achieves state of the art results on the automated metrics across four major D2T datasets.
  arXiv  Detail & Related papers  (2020-04-08T11:16:53Z)

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.