| Challenge: | Existing methods for data-to-text generation focus on learning correspondences between structured data and associated texts. |
| Approach: | They aim to bootstrap generators from large scale datasets where data and related texts are loosely aligned. |
| Outcome: | The proposed model improves on a vanilla encoder-decoder which relies on soft attention. |
Similar Papers
Neural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondence (2020.acl-main)
Copied to clipboard
| Challenge: | Recent neural attention models conflate all steps into a single end-to-end system and simplify training process. |
| Approach: | They propose to explicitly segment target text into fragment units and align them with their data correspondences. |
| Outcome: | The proposed model outperforms neural attention models on E2E and WebNLG benchmarks. |
Data-to-text Generation with Macro Planning (2021.tacl-1)
Copied to clipboard
| Challenge: | Recent approaches to data-to-text generation adopt the encoder-decoder architecture . however, these models perform poorly at selecting appropriate content and ordering it coherently . |
| Approach: | They propose a neural model with a macro planning stage followed by a generation stage . they use data from databases of records, simulations of physical systems, accounting spreadsheets . |
| Outcome: | The proposed model outperforms baselines on two data-to-text benchmarks . it uses the encoderdecoder architecture and is compared with existing models . |
Select and Attend: Towards Controllable Content Selection in Text Generation (D19-1)
Copied to clipboard
| Challenge: | Recent neural network models conflate content selection and surface realization into a black-box architecture, resulting in content to be described in text cannot be explicitly controlled. |
| Approach: | They propose to decouple content selection from the decoder to allow finer-grained control over the generation. |
| Outcome: | The proposed model can be trained end-to-end without human annotations and achieves promising results in data-totext and headline generation tasks. |
Controlled Hallucinations: Learning to Generate Faithfully from Noisy Data (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Neural text generation (data- or text-to-text) demonstrates remarkable performance when training data is abundant which for many applications is not the case. |
| Approach: | They propose a technique to treat hallucinations as a controllable aspect of the generated text without dismissing any input and without modifying the model architecture. |
| Outcome: | The proposed technique can be used on a WikiBio dataset and in a human evaluation. |
Advances in Pre-Training Distributed Word Representations (L18-1)
Copied to clipboard
| Challenge: | Pre-trained word representations are a building block of many Natural Language Processing and Machine Learning applications. |
| Approach: | They propose to combine known tricks and a set of publicly available pre-trained word vector representations to train high-quality representations. |
| Outcome: | The proposed models outperform the current state of the art on a number of tasks while maintaining a high training speed to scale to massive amount of data. |
WikiTableT: A Large-Scale Data-to-Text Dataset for Generating Wikipedia Article Sections (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing datasets for data-to-text generation focus on single-sentence generation or long-form generation. |
| Approach: | They create a dataset that pairs Wikipedia sections with tabular data and various metadata. |
| Outcome: | The proposed dataset can generate fluent and high quality texts but struggle with coherence and factuality. |
Data-to-text Generation by Splicing Together Nearest Neighbors (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing work on data-to-text generation relies on retrieved "neighbors" but instead generates text token-by-token, left-to right. |
| Approach: | They propose to splice together retrieved segments of text from "neighbor" source-target pairs to generate text token-by-token, left-to-right. |
| Outcome: | The proposed method performs on par with strong baselines in terms of automatic and human evaluation, but allows for more interpretable and controllable generation. |
Little Giants: Synthesizing High-Quality Embedding Data at Scale (2025.naacl-long)
Copied to clipboard
| Challenge: | Synthetic data generation is an increasingly popular way of training models without the need for large, manually labeled datasets. |
| Approach: | They propose a framework that aligns open-source small models to efficiently generate large-scale embedding data. |
| Outcome: | The proposed framework outperforms state-of-the-art embedding models by using only 1/10 of the GPT API calls. |
Domain-matched Pre-training Tasks for Dense Retrieval (2022.findings-naacl)
Copied to clipboard
Barlas Oguz, Kushal Lakhotia, Anchit Gupta, Patrick Lewis, Vladimir Karpukhin, Aleksandra Piktus, Xilun Chen, Sebastian Riedel, Scott Yih, Sonal Gupta, Yashar Mehdad
| Challenge: | Existing approaches to improve performance of pre-training tasks are needed. |
| Approach: | They propose to pre-train large bi-encoder models on a recently released set of 65 millionsynthetically generated questions and 200 million post-comment pairs from a preexisting reddit conversation dataset. |
| Outcome: | The proposed model can be pre-trained on a set of 65 millionsynthetically generated questions and 200 million post-comment pairs from a preexisting dataset of Reddit conversations. |
STable: Table Generation Framework for Encoder-Decoder Models (2024.eacl-long)
Copied to clipboard
Michał Pietruszka, Michał Turski, Łukasz Borchmann, Tomasz Dwojak, Gabriela Nowakowska, Karolina Szyndler, Dawid Jurkiewicz, Łukasz Garncarek
| Challenge: | Existing approaches to infer text-to-table neural models are limited to raw text, but the proposed framework is capable of unifying a variety of problems involving natural language. |
| Approach: | They propose a framework for text-to-table neural models that utilizes a generalized sequential method that comprehends information from all cells in the table. |
| Outcome: | The proposed framework outperforms previous approaches on several challenging datasets and outperformed existing models by up to 15%. |