Table-to-text Generation by Structure-aware Seq2seq Learning (1711.09724v1)

Abstract: Table-to-text generation aims to generate a description for a factual table which can be viewed as a set of field-value records. To encode both the content and the structure of a table, we propose a novel structure-aware seq2seq architecture which consists of field-gating encoder and description generator with dual attention. In the encoding phase, we update the cell memory of the LSTM unit by a field gate and its corresponding field value in order to incorporate field information into table representation. In the decoding phase, dual attention mechanism which contains word level attention and field level attention is proposed to model the semantic relevance between the generated description and the table. We conduct experiments on the \texttt{WIKIBIO} dataset which contains over 700k biographies and corresponding infoboxes from Wikipedia. The attention visualizations and case studies show that our model is capable of generating coherent and informative descriptions based on the comprehensive understanding of both the content and the structure of a table. Automatic evaluations also show our model outperforms the baselines by a great margin. Code for this work is available on https://github.com/tyliupku/wiki2bio.

• The paper introduces a structure-aware seq2seq architecture that integrates table content with field information to generate coherent natural language descriptions.
• It employs a field-gating LSTM encoder and dual attention mechanisms at both word and field levels to effectively align table data with generated text.
• Experimental results on the WIKIBIO dataset demonstrate significant improvements in BLEU-4 and ROUGE-4 scores, highlighting its potential for automated report generation.

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

The paper "Table-to-text Generation by Structure-aware Seq2seq Learning" addresses the challenge of generating descriptive natural language from structured data, specifically focusing on tables such as Wikipedia infoboxes. The proposed solution builds on a novel sequence-to-sequence (seq2seq) model that incorporates structural understanding of tables to produce coherent textual descriptions effectively.

Overview

The core contribution of the paper is a structure-aware seq2seq architecture that is designed to integrate content and structural information of tables into the text generation process. The model operates in two main phases: encoding and decoding. During the encoding phase, the model uses a field-gating variant of LSTM (Long Short-Term Memory) units, augmented by a field gate, to update cell memory with field information. This is critical for capturing the nuanced structure of input tables. In the decoding phase, a dual attention mechanism is introduced to enhance the generation process. This mechanism includes both word-level and field-level attentions that enable the model to maintain alignment between the generated text and the relational structure of the table content.

Methodology

1. Field-gating Table Encoder: The encoder represents each word in the table alongside its corresponding field information. By incorporating field embedding through a field gate, the encoder integrates content and structured representations, allowing for better inferencing over complex table data.
2. Dual Attention Mechanism: This novel attention mechanism facilitates the handling of both local and global addressing of table content, thereby improving the alignment between content in the infobox and the generated descriptive text. Word-level attention ensures the coherence within sentence structures, while field-level attention accounts for the relevance of fields across the table, crucial for maintaining the relationships between different segments of the input data.

Experimental Results

The experiments conducted using the WIKIBIO dataset—comprising over 700,000 biographies and corresponding table data—demonstrate the model's capability to surpass existing statistical and basic seq2seq models significantly. The model showed substantial improvements in BLEU-4 and ROUGE-4 scores compared to baselines, attesting to its effectiveness in generating comprehensive descriptions that are semantically aligned with table content.

Implications and Future Work

The implications of this work are notable in fields where structured data to natural language translation is essential, such as automated report generation, digital assistants, and knowledge management systems. By embedding structural understanding into LLMs, this approach offers a scalable solution for various domains requiring narrative descriptions from tabular datasets. The methodology could be extended to explore other structured data formats and more complex relationships within input data, potentially improving the robustness and applicability of AI systems in real-world tasks.

Future developments could focus on refining the dual attention mechanism and extending it to larger and more diverse datasets, potentially integrating with state-of-the-art models like transformer architectures that may offer more computational efficiency and improved textual coherence in highly structured environments. Additionally, addressing challenges such as the varied schemas in tables from different domains and effective handling of less frequent or novel fields remains an area for further investigation.

In summary, the paper presents a significant step forward in the domain of data-to-text generation by innovatively marrying the seq2seq model framework with advanced attention mechanisms, paving the way for enhanced interpretability and utility of machine-generated text from complex structured data.