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natural-language-processing-6

Table-to-Text Generation

3260 papers • 126 benchmarks • 313 datasets

Table-to-Text Generation is to generate a description from the structured table. Source: Key Fact as Pivot: A Two-Stage Model for Low Resource Table-to-Text Generation

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Benchmarks

These leaderboards are used to track progress in table-to-text-generation-6

Trend

Dataset

Best Model

Actions

WikiBio

WikiBio

Wikipedia Person and Animal Dataset

Wikipedia Person and Animal Dataset

Libraries

i

Use these libraries to find table-to-text-generation-6 models and implementations

Datasets

WebNLG

WikiBio

RotoWire

E2E

DART

Wikipedia Person and Animal Dataset

Wikipedia Person and Animal Dataset

Subtasks

KB-to-Language Generation

Most implemented papers

Prefix-Tuning: Optimizing Continuous Prompts for Generation

Percy Liang, Xiang Lisa Li•Thu Dec 31 2020

Prefix-tuning is proposed, a lightweight alternative to fine- Tuning for natural language generation tasks, which keeps language model parameters frozen and instead optimizes a sequence of continuous task-specific vectors, which is called the prefix.

5330

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

WebNLG (Seen)

WebNLG (Seen)

WebNLG (Unseen)

WebNLG (Unseen)

WebNLG (All)

WebNLG (All)

E2E

E2E

DART

DART

RotoWire

RotoWire

0

Neural Text Generation from Structured Data with Application to the Biography Domain

Michael Auli, David Grangier, R. Lebret•Wed Mar 23 2016

A neural model for concept-to-text generation that scales to large, rich domains and significantly out-performs a classical Kneser-Ney language model adapted to this task by nearly 15 BLEU is introduced.

564 0

Table-to-text Generation by Structure-aware Seq2seq Learning

Lei Sha, Zhifang Sui, Tianyu Liu, Kexiang Wang, Baobao Chang•Sun Nov 26 2017

The attention visualizations and case studies show that the novel structure-aware seq2seq architecture which consists of field-gating encoder and description generator with dual attention is capable of generating coherent and informative descriptions based on the comprehensive understanding of both the content and the structure of a table.

280 0

Order-Planning Neural Text Generation From Structured Data

Lei Sha, Zhifang Sui, Tianyu Liu, Lili Mou, Sujian Li, Baobao Chang, P. Poupart•Thu Aug 31 2017

This paper proposes an order-planning text generation model, where order information is explicitly captured by link-based attention and a self-adaptive gate combines the link- based attention with traditional content-based Attention.

117 0

Describing a Knowledge Base

Qingyun Wang, Lifu Huang, Boliang Zhang, Heng Ji, Kevin Knight, Xiaoman Pan•Wed Sep 05 2018

This work builds a generation framework based on a pointer network which can copy facts from the input KB, and adds two attention mechanisms: (i) slot-aware attention to capture the association between a slot type and its corresponding slot value; and (ii) a new table position self-attention to captured the inter-dependencies among related slots.

58 0

Handling Divergent Reference Texts when Evaluating Table-to-Text Generation

Manaal Faruqui, William W. Cohen, Ankur P. Parikh, Ming-Wei Chang, Bhuwan Dhingra, Dipanjan Das•Sun Jun 02 2019

A new metric is proposed, PARENT, which aligns n-grams from the reference and generated texts to the semi-structured data before computing their precision and recall, and is applicable when the reference texts are elicited from humans using the data from the WebNLG challenge.

218 0

Key Fact as Pivot: A Two-Stage Model for Low Resource Table-to-Text Generation

Xu Sun, Shuming Ma, Peng Li, Tianyu Liu, Jie Zhou, Pengcheng Yang•Sun Jun 30 2019

A novel model is proposed to separate the generation of table-to-text generation into two stages: key fact prediction and surface realization, which needs much fewer annotated data and can be trained with pseudo parallel corpus.

50 0

Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time)

Ting Liu, Bing Qin, Xiaocheng Feng, Heng Gong•Mon Dec 31 2018

This work develops a table cell fusion gate to combine representations from row, column and time dimension into one dense vector according to the saliency of each dimension’s representation.

64 0

Two Birds, One Stone: A Simple, Unified Model for Text Generation from Structured and Unstructured Data

Jimmy J. Lin, H. Shahidi, Ming Li•Sun Sep 22 2019

Experimental results demonstrate that a basic attention-based seq2seq model trained with the exponential moving average technique achieves the state of the art in both neural table-to-text generation and neural question generation tasks for text generation from structured and unstructured data.

16 0

Variational Template Machine for Data-to-Text Generation

Lei Li, Hao Zhou, Rong Ye, Zhongyu Wei, Wenxian Shi•Mon Feb 03 2020

This paper proposes the variational template machine (VTM), a novel method to generate text descriptions from data tables, and utilizes both small parallel data and large raw text without aligned tables to enrich the template learning.

45 0

Adding a benchmark result helps the community track progress.

Table-to-Text Generation | State-of-the-Art