Simplification Using Paraphrases and Context-Based Lexical Substitution

Kriz, Reno; Miltsakaki, Eleni; Apidianaki, Marianna; Callison-Burch, Chris

doi:10.18653/v1/n18-1019

Cited by 18 publications

(12 citation statements)

References 16 publications

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“…Fader et al (2013) and Dong et al (2017) employ paraphrase knowledge to enhance question answering models. Kriz et al (2018) utilize paraphrase and context-based lexical substitution knowledge to improve simplification task. Similarly, Zhao et al (2018a) combine paraphrase rules of PPDB (Ganitkevitch et al, 2013) with Transformer (Vaswani et al, 2017) to perform sentence simplification task.…”

Section: Related Workmentioning

confidence: 99%

How to Ask Good Questions? Try to Leverage Paraphrases

Jia

Zhou

Sun

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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Given a sentence and its relevant answer, how to ask good questions is a challenging task, which has many real applications. Inspired by human's paraphrasing capability to ask questions of the same meaning but with diverse expressions, we propose to incorporate paraphrase knowledge into question generation(QG) to generate human-like questions. Specifically, we present a two-hand hybrid model leveraging a self-built paraphrase resource, which is automatically conducted by a simple back-translation method. On the one hand, we conduct multi-task learning with sentence-level paraphrase generation (PG) as an auxiliary task to supplement paraphrase knowledge to the task-share encoder. On the other hand, we adopt a new loss function for diversity training to introduce more question patterns to QG. Extensive experimental results show that our proposed model obtains obvious performance gain over several strong baselines, and further human evaluation validates that our model can ask questions of high quality by leveraging paraphrase knowledge.

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Section: Related Workmentioning

confidence: 99%

How to Ask Good Questions? Try to Leverage Paraphrases

Jia

Zhou

Sun

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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“…Extending the complex word identification model of Kriz et al (2018), we train a linear regression model using length, number of syllables, and word frequency; we also include Word2Vec embeddings (Mikolov et al, 2013). To collect data for this task, we consider the Newsela corpus, a collection of 1,840 news articles written by professional editors at 5 reading levels .…”

Section: Word Complexity Predictionmentioning

confidence: 99%

Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification

Kriz¹,

Sedoc²,

Apidianaki³

et al. 2019

Proceedings of the 2019 Conference of the North

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Sentence simplification is the task of rewriting texts so they are easier to understand. Recent research has applied sequence-to-sequence (Seq2Seq) models to this task, focusing largely on training-time improvements via reinforcement learning and memory augmentation. One of the main problems with applying generic Seq2Seq models for simplification is that these models tend to copy directly from the original sentence, resulting in outputs that are relatively long and complex. We aim to alleviate this issue through the use of two main techniques. First, we incorporate content word complexities, as predicted with a leveled word complexity model, into our loss function during training. Second, we generate a large set of diverse candidate simplifications at test time, and rerank these to promote fluency, adequacy, and simplicity. Here, we measure simplicity through a novel sentence complexity model. These extensions allow our models to perform competitively with state-of-the-art systems while generating simpler sentences. We report standard automatic and human evaluation metrics. 1

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“…Lexical simplification: Prior work on lexical simplification depends on lexical and corpusbased features to assess word complexity. For complex word identification, there are broadly two lines of research: learning a frequency-based threshold over a large corpus (Shardlow, 2013b) or training an ensemble of classifiers over a combination of lexical and language model features (Shardlow, 2013a;Paetzold and Specia, 2016a;Yimam et al, 2017;Kriz et al, 2018). Substitution ranking also follows similar trend.…”

Section: Related Workmentioning

confidence: 99%

A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification

Maddela¹,

Xu²

2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

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Current lexical simplification approaches rely heavily on heuristics and corpus level features that do not always align with human judgment. We create a human-rated wordcomplexity lexicon of 15,000 English words and propose a novel neural readability ranking model with a Gaussian-based feature vectorization layer that utilizes these human ratings to measure the complexity of any given word or phrase. Our model performs better than the state-of-the-art systems for different lexical simplification tasks and evaluation datasets. Additionally, we also produce SimplePPDB++, a lexical resource of over 10 million simplifying paraphrase rules, by applying our model to the Paraphrase Database (PPDB). 1

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Simplification Using Paraphrases and Context-Based Lexical Substitution

Cited by 18 publications

References 16 publications

How to Ask Good Questions? Try to Leverage Paraphrases

How to Ask Good Questions? Try to Leverage Paraphrases

Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification

A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification

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