A Hierarchical Neural Autoencoder for Paragraphs and Documents (1506.01057v2)

Abstract: Natural language generation of coherent long texts like paragraphs or longer documents is a challenging problem for recurrent networks models. In this paper, we explore an important step toward this generation task: training an LSTM (Long-short term memory) auto-encoder to preserve and reconstruct multi-sentence paragraphs. We introduce an LSTM model that hierarchically builds an embedding for a paragraph from embeddings for sentences and words, then decodes this embedding to reconstruct the original paragraph. We evaluate the reconstructed paragraph using standard metrics like ROUGE and Entity Grid, showing that neural models are able to encode texts in a way that preserve syntactic, semantic, and discourse coherence. While only a first step toward generating coherent text units from neural models, our work has the potential to significantly impact natural language generation and summarization\footnote{Code for the three models described in this paper can be found at www.stanford.edu/~jiweil/ .

A Hierarchical Neural Autoencoder for Paragraphs and Documents

Overview

The paper "A Hierarchical Neural Autoencoder for Paragraphs and Documents" presents a novel approach in the domain of natural language generation (NLG) by focusing on the coherent reconstruction of multi-sentence paragraphs using Long Short-Term Memory (LSTM) autoencoders. This research addresses the challenge of preserving syntactic, semantic, and discourse coherence in long texts, providing a foundational step towards generating coherent text units for complex NLG tasks such as summarization and dialogue systems.

Methodology

The paper introduces a hierarchical model wherein an LSTM autoencoder captures both sentence-level and document-level representations. The approach consists of two models: a Standard LSTM, treating the document as a flat sequence of tokens, and a Hierarchical LSTM, which adopts a structured approach by encoding tokens at the sentence level before forming paragraph representations. A hierarchical composition is achieved by stacking LSTM layers, thereby capturing compositionality on multiple levels.

A key innovation in the hierarchical model is the incorporation of an attention mechanism at the sentence level, enhancing the model's ability to focus on specific input sequences during the decoding process. This facilitates improved performance by linking each decoding step with relevant input segments, thus preserving coherence and meaning.

Implementation and Evaluation

Experiments were conducted on two datasets: a domain-specific set of hotel reviews and an open-domain Wikipedia dataset. The hierarchical models demonstrated superior performance over the standard sequence-to-sequence models, particularly benefiting from the attentional enhancements. Evaluation metrics included BLEU and ROUGE scores, indicating progress in syntactic and semantic fidelity, while a custom coherence score assessed the preservation of input-output sentence ordering.

Notably, the hierarchical LSTM with attention achieved a coherence score of 1.57 on the hotel-review dataset, reflecting minimal deviation from the original sentence order, a crucial aspect of discourse coherence in text generation tasks.

Implications and Future Directions

This research underscores the potential of hierarchical neural networks in complex NLG tasks by successfully encoding and reconstructing extended text sequences with preserved coherence. While the current work focuses on autoencoding, this methodology can be transitioned to more advanced applications such as summarization and question answering, where input-output transformations differ significantly.

The results point to the effectiveness of addressing hierarchical and compositional structures in text. Future work could involve exploring deeper architectures, larger datasets, and incorporating additional context, such as user-specific information in dialogue systems, to enhance the applicability and robustness of the models.

Conclusion

The paper provides a valuable contribution to natural language processing by demonstrating the feasibility of generating coherent multi-sentence text through hierarchical LSTM autoencoders. The integration of attention mechanisms marks a significant advancement, facilitating the task of maintaining discourse coherence. As this research matures, it promises to influence the development of sophisticated NLG systems capable of undertaking more intricate text generation tasks effectively.