Neural Paraphrase Generation with Stacked Residual LSTM Networks (1610.03098v3)

Abstract: In this paper, we propose a novel neural approach for paraphrase generation. Conventional para- phrase generation methods either leverage hand-written rules and thesauri-based alignments, or use statistical machine learning principles. To the best of our knowledge, this work is the first to explore deep learning models for paraphrase generation. Our primary contribution is a stacked residual LSTM network, where we add residual connections between LSTM layers. This allows for efficient training of deep LSTMs. We evaluate our model and other state-of-the-art deep learning models on three different datasets: PPDB, WikiAnswers and MSCOCO. Evaluation results demonstrate that our model outperforms sequence to sequence, attention-based and bi- directional LSTM models on BLEU, METEOR, TER and an embedding-based sentence similarity metric.

• The paper introduces a novel stacked residual LSTM model that effectively mitigates gradient issues in deep network training.
• It employs residual connections within an encoder-decoder framework to generate quality paraphrases across datasets like PPDB, WikiAnswers, and MSCOCO.
• Experimental results demonstrate significant improvements in BLEU, METEOR, and TER metrics over traditional sequence-to-sequence approaches.

Overview of Neural Paraphrase Generation with Stacked Residual LSTM Networks

The paper presents a novel deep learning approach for paraphrase generation using a stacked residual Long Short-Term Memory (LSTM) network architecture. Unlike traditional methods leveraging linguistic rules, thesaurus-based alignments, or statistical models, this paper pioneers the application of deep learning to the task. The proposed model introduces residual connections between LSTM layers to facilitate the effective training of deep neural networks, an approach inspired by the success of residual connections in Convolutional Neural Networks (CNNs).

The paper details experiments conducted on three distinct datasets: the Paraphrase Database (PPDB), WikiAnswers, and MSCOCO, demonstrating the model's superiority over existing sequence-to-sequence and bi-directional LSTM models across standard evaluation metrics.

Model and Methodology

The proposed model consists of multiple layers of stacked LSTM units with residual connections incorporated to allow the gradients to propagate efficiently through deeper architectures. This compensates for the typical limitations of vanishing or exploding gradients in deep LSTM networks. Furthermore, the paper employs an encoder-decoder framework to transform source sequences into target paraphrase sequences.

The model's use of residual connections in LSTMs is innovative, leveraging principles that have shown strong results in image recognition and adapting these for NLP tasks. By adding direct connections that bypass intermediate LSTM layers, the model can effectively stack a higher number of LSTM layers without suffering from degradation problems, a typical challenge in deeper RNN models.

Datasets and Experimental Setup

The evaluation was carried out using three datasets:

1. PPDB: A large dataset consisting of lexical, phrasal, and syntactic paraphrases, selected here for assessing short sequence paraphrasing abilities.
2. WikiAnswers: This corpus includes question paraphrases curated from WikiAnswers, offering a unique set for evaluating the model on real-life paraphrastic variances in a question-answering format.
3. MSCOCO: Primarily an image caption dataset used here without the images, exploiting the implicit paraphrase-like nature of multiple captions per image. It helps validate the model's performance in a contextually diverse set of text data.

The model is trained using a stochastic gradient descent (SGD) technique with a focus on lowering perplexity, indicating lower uncertainty in paraphrase predictions.

Results and Analysis

The evaluation results indicate that the proposed stacked residual LSTM model outperforms competing models across BLEU, METEOR, and Translation Error Rate (TER) metrics. The model shows a particularly notable improvement in BLEU scores, suggesting enhanced capability in generating high-quality paraphrases congruent with human-like variance.

It was observed that while residual connections boosted performance by mitigating gradient-related issues, the training convergence issues, particularly with shorter sequences in PPDB, bring attention to dataset-specific characteristics influencing model behavior.

Implications and Future Work

The strong performance of stacked residual LSTMs in paraphrase generation suggests potential broad applications across various NLP tasks requiring language variability, including machine translation, text summarization, and content rewriting. The adaptation of residual networks from vision to language tasks represents a promising research direction within deep learning, underscoring the portability of deep learning techniques across domains.

Future work could explore integrating memory augmentation techniques to enhance context understanding or employing unsupervised learning paradigms to reduce dependency on large labeled datasets. Additionally, leveraging visual inputs for multi-modal paraphrasing tasks or examining the model's performance on low-resource languages could provide further insights into the versatility of deep paraphrase generation systems.

The paper makes a compelling case for the effectiveness of deep learning in paraphrase generation, setting a new benchmark for future research in this area.