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Integrating Transformer and Paraphrase Rules for Sentence Simplification
Sanqiang Zhao, Rui Meng, Daqing He, Saptono Andi, Parmanto Bambang
TL;DR
Sentence simplification seeks to reduce complexity while preserving meaning, but neural models may miss infrequent rules and lack sufficient training-data coverage. This paper combines multi-layer, multi-head attention with two approaches for integrating Simple PPDB. The proposed models outperform previous methods, and DMASS+DCSS provide good rule coverage and accuracy.
Problem
Neural simplification models may not have broad enough training data and can overlook significant infrequent transformations.
Source: §1 · p. 1Method
The paper uses multi-layer, multi-head attention and integrates Simple PPDB through DMASS rule memory and DCSS shared-parameter encoding.
Source: §1 · p. 2Results
Takeaways & Limitations
The experiments support integrating external simplification knowledge with neural models to improve performance and rule utilization.
Source: §5 · pp. 8–9
Abstract
from arXiv · showhide
Sentence simplification aims to reduce the complexity of a sentence while retaining its original meaning. Current models for sentence simplification adopted ideas from ma- chine translation studies and implicitly learned simplification mapping rules from normal- simple sentence pairs. In this paper, we explore a novel model based on a multi-layer and multi-head attention architecture and we pro- pose two innovative approaches to integrate the Simple PPDB (A Paraphrase Database for Simplification), an external paraphrase knowledge base for simplification that covers a wide range of real-world simplification rules. The experiments show that the integration provides two major benefits: (1) the integrated model outperforms multiple state- of-the-art baseline models for sentence simplification in the literature (2) through analysis of the rule utilization, the model seeks to select more accurate simplification rules. The code and models used in the paper are available at https://github.com/ Sanqiang/text_simplification.
1 Introduction
Sentence simplification seeks simpler wording while preserving meaning, but neural models have lagged statistical models and may miss infrequent rules. The paper proposes a multi-layer, multi-head attention architecture and two ways to integrate human-curated simplification rules, reporting better performance than prior models with good rule coverage and accuracy.
1 Introduction
- Sentence simplification reduces sentence complexity while retaining meaning, with potential benefits for people with low literacy, children, non-native speakers, and people with language impairments.Source: §1 · p. 1
- Neural models learn simplification rules automatically but require substantial training data, and their performance has remained below statistical models in this task.The authors speculate that training datasets may not be large or broad enough to cover common simplification situations.Source: §1 · p. 1
- Neural networks may overlook significant but infrequent transformations because shared parameters favor frequent rules and sentence-pair training can treat rare rules as noise.The authors propose individual rule memory as one way to prevent forgetting low-frequency rules and distinguish rules from noise.Source: §1 · p. 1
- The paper proposes multi-layer, multi-head attention to select words in normal sentences and simplify them more accurately than RNN/LSTM models.Source: §1 · p. 2
- DMASS adds memory to recognize each rule's context and output, while DCSS encodes rules in shared parameters and revises the loss to encourage less frequent rules.Source: §1 · pp. 1–2
- The proposed models outperform previous sentence simplification models while achieving good coverage of rules to apply and accuracy in applying correct rules.The paper reports these outcomes in its empirical study.Source: §1 · p. 2
2 Related Work
Prior work on sentence simplification includes statistical translation-inspired models and neural models that use reinforcement learning. This paper addresses neural models' limited integration of external knowledge and their tendency to neglect infrequent rules through an external knowledge base and augmented memory.
2 Related Work
- Earlier statistical simplification work used a tree-based model inspired by statistical machine translation [Zhu et al. (2010)], quasi-synchronous grammar and integer programming [Woodsend and Lapata (2011)], and two-stage phrase-based PBMT with reranking [Wubben et al. (2012)].Source: §2 · p. 2
- SBMT-SARI used an external knowledge base and achieved state-of-the-art performance [Xu et al. (2016)], while DRESS and DRESS-LS used reinforcement learning to reward simpler outputs [Zhang and Lapata (2017)].The passage says the latter models remained limited by a lack of external knowledge.Source: §2 · p. 2
- Augmented dynamic memory has been explored for language models [Daniluk et al., 2017; Grave et al., 2016], question answering [Miller et al., 2016], and machine translation [Feng et al., 2017; Tu et al., 2017].The authors connect this work to sentence simplification's tendency to neglect infrequent rules.Source: §2 · p. 2
3 Our Sentence Simplification Models
The paper describes a Transformer-based sentence simplification model and two approaches for integrating Simple PPDB rules: DCSS modifies training loss, while DMASS uses dynamic memory.
3.1 Multi-Layer, Multi-Head Attention
- The Transformer simplification model maps normal input to simple output using stacked attention layers, with decoder attention over encoder outputs.Source: §3.1 · pp. 2–3
3.2 Integrating with Simple PPDB
- Simple PPDB supplies 4.5 million weighted, syntactically typed normal-to-simple phrase rules; the authors report this as the first integration of such a knowledge base with neural models.Source: §3.2 · p. 3
3.2.1 Deep Critic Sentence Simplification Model (DCSS)
- DCSS adds a rule-focused loss that boosts simplified outputs and penalizes original forms, reweighting words by Simple PPDB rule weights to counter infrequent-rule neglect.Source: §3.2.1 · p. 4
3.2.2 Deep Memory Augmented Sentence Simplification Model (DMASS)
- DMASS targets shared-parameter models’ tendency to favor frequent rules, which leaves important infrequent rules underused, especially with limited training data.Source: §3.2.2 · p. 4
- DMASS stores per-rule key-value pairs: the current context queries rules, and similarity weights combine their output vectors into the memory read.Source: §3.2.2 · p. 4
- After prediction, DMASS appends a new context-output pair when a rule is absent or updates an existing key and value by averaging them with the current vectors.Source: §3.2.2 · p. 5
4 Experiments
The experiments compare Transformer configurations and Simple PPDB integrations on Turk and Newsela, using SARI, FKGL, and rule utilization to assess simplification. DMASS+DCSS achieves the best SARI result, while the analyses also identify trade-offs in rule accuracy and sentence-length simplification.
4 Experiments
- Training uses 296,402 WikiLarge sentence pairs; Turk provides 2,000 validation and 356 test samples, and Newsela tests transfer to another corpus style.The experiments report SARI, FKGL, and the precision, recall, and F1 of applying correct simplification rules.Source: §4 · pp. 5–6
4.1 Impacts of Multi-Layer, Multi-Head Attention Architecture
- Transformer models outperform RNN/LSTM on SARI and FKGL, with more layers or heads generally improving both metrics and reducing sentence and word lengths.The reported improvements are for models not yet integrated with Simple PPDB.Source: §4.1 · p. 6
- Higher-capacity Transformers improve addition and deletion F1, but may add inaccurate words and lower keeping F1.The authors suggest Simple PPDB could guide whether and how words should be simplified.Source: §4.1 · p. 6
4.2 Impacts of Integrating the Simple PPDB
- DMASS+DCSS achieves the best SARI score among the evaluated models on Turk and Newsela.The authors attribute this result to integrating Simple PPDB and combining the models’ rule-storage approaches.Source: Table 3 · p. 7, Table 4 · p. 7, §4.2 · p. 8
- Hybrid has the lowest FKGL, while DRESS and DRESS-LS rank next; these systems shorten sentences, whereas SBMT-SARI and the proposed models generate shorter words.FKGL does not account for reference simplifications, so its simplicity scores may come at the cost of information and readability.Source: §4.2 · pp. 7–8
- DMASS+DCSS benefits from both additional and internal rule storage, while SBMT-SARI’s aggressive simplification yields high recall but weaker precision.DMASS performs better than DCSS in rule utilization; the combined model is reported to apply more accurate rules.Source: §4.2 · p. 8
- Larger beam search size correlates strongly with rule-utilization performance and is reported to improve rule coverage and accuracy.The authors also report that larger beams mitigate inaccurate simplification, so fewer words are simplified and average word length rises slightly.Source: §4.2 · p. 8
5 Conclusion
The paper proposes neural sentence simplification using multi-layer, multi-head attention and two approaches to integrate Simple PPDB. The proposed models outperform multiple state-of-the-art baselines, with DMASS+DCSS offering both rule coverage and accuracy.
5 Conclusion
- The proposed neural models combine multi-layer, multi-head attention with different ways of integrating Simple PPDB.The authors report that the architecture can select specific words in a normal sentence and choose simplified words.Source: §5 · p. 8
- The proposed models outperform multiple state-of-the-art baselines, and DMASS+DCSS provides both good rule coverage and accuracy.The authors identify further study of the distinct effects of additional and internal memory as future work.Source: §5 · pp. 8–9