- The paper introduces DRESS, a reinforcement learning framework that integrates an attention-based encoder-decoder model to simplify sentences effectively.
- It employs a reward function optimized via the REINFORCE algorithm to balance simplicity, fluency, and meaning preservation using the SARI metric.
- Experiments on Newsela, WikiSmall, and WikiLarge datasets demonstrate improved performance over state-of-the-art simplification systems.
Sentence Simplification with Deep Reinforcement Learning
Sentence simplification is a vital NLP task that aims to reduce linguistic complexity while preserving essential information and meaning. This paper by Xingxing Zhang and Mirella Lapata addresses the sentence simplification challenge with a novel approach that combines an encoder-decoder model with a deep reinforcement learning framework, named DRESS (Deep REinforcement Sentence Simplification).
The approach utilizes insights from neural machine translation, particularly focusing on the encoder-decoder architecture implemented with recurrent neural networks. Central to this method is the optimization of a reward function that balances simplicity, fluency, and meaning preservation during the simplification process. The paper highlights DRESS's superior performance over existing simplification systems by experimentally evaluating it on three different datasets: Newsela, WikiSmall, and WikiLarge.
Methodology
The core of the proposed model is an attention-based encoder-decoder structure coupled with reinforcement learning, specifically applying the REINFORCE algorithm. The model iteratively explores simplification options to maximize an expected reward, which accounts for simplification-specific constraints. A novel aspect of this work is the use of the SARI metric to capture sentence simplicity. SARI evaluates outputs by comparing them against both reference simplifications and the original complex sentences.
Furthermore, the authors address the lexical simplification aspect by leveraging attention scores from a pre-trained encoder-decoder model to align complex and simple sentences, thereby explicitly encouraging beneficial lexical substitutions.
Experiments and Results
The model's efficacy is demonstrated through extensive experiments on three datasets. On the Newsela corpus, which consists of professionally simplified news articles, the proposed model outperforms both the phrase-based machine translation with reranking (PBMT-R) system and the Hybrid model, particularly excelling in fluency and simplicity metrics. For the WikiSmall dataset, the model shows competitive performance, showcasing its adaptability and robustness across varying data characteristics. Finally, on the WikiLarge corpus, the model achieves promising BLEU scores and outperforms traditional systems on fluency scores in human evaluations.
Human evaluations were also conducted, assessing fluency, adequacy, and simplicity of each test output, consistently reflecting the model's strength in producing fluent and adequately simplified text.
Implications and Future Work
This research provides substantial insights and methodologies that could extend beyond sentence simplification to other NLP tasks, including summarization and translation. The use of reinforcement learning to seamlessly integrate multiple optimization objectives like simplicity, fluency, and semantic fidelity is a promising direction that could be applied to multi-objective optimization problems in NLP.
Future research could explore integrating more sophisticated sentence splitting techniques or advancing document-level simplification strategies. Expanding the reinforcement learning approach to other text generation tasks such as story or poem generation is another intriguing research avenue.
Overall, this paper presents a solid advancement in sentence simplification using deep reinforcement learning, offering a robust framework that balances critical objectives in text generation tasks. The implications of this work extend to various applications across NLP and can inspire further research into complex sequence transformation tasks.