| Challenge: | a dataset mapping a complex sentence to a sequence of sentences conveying the same meaning is challenging in NLP. |
| Approach: | They propose a neural split and a copy-mechanism to break a complex sentence into several shorter sentences that convey the same meaning. |
| Outcome: | The proposed model outperforms the baseline model by 8.68 BLEU and further improves on the task. |
Similar Papers
Small but Mighty: New Benchmarks for Split and Rephrase (2020.emnlp-main)
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| Challenge: | Split and Rephrase is a text simplification task that requires a strong evaluation benchmark and metric . despite its relatively new nature, the benchmark dataset contains easily exploitable syntactic cues . |
| Approach: | They propose to use crowdsourced datasets to evaluate split and rephrase models . they find that the widely used benchmark dataset universally contains exploitable syntactic cues . |
| Outcome: | The proposed model performs better than the state-of-the-art model, the authors say . they show that the datasets contain significantly more diverse syntax . |
BiSECT: Learning to Split and Rephrase Sentences with Bitexts (2021.emnlp-main)
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| Challenge: | Several past efforts have created Split and Rephrase training sets, which consist of long, complex input sentences paired with multiple shorter sentences that preserve the meaning of the input sentence. |
| Approach: | They propose a new dataset and a model for this task by extracting 1-2 sentence alignments from bilingual parallel corpora and using machine translation to convert both sides of the corpus into the same language. |
| Outcome: | The proposed model can perform a wider variety of split operations and improve upon previous state-of-the-art approaches in automatic and human evaluations. |
WikiSplit++: Easy Data Refinement for Split and Rephrase (2024.lrec-main)
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| Challenge: | Existing text simplification methods rely on encoder-decoder models to achieve this task. |
| Approach: | They propose a text-to-text generation approach that applies encoder-decoder models to a large-scale dataset to improve Split and Rephrase. |
| Outcome: | The proposed approach improves Split and Rephrase readability and performance on large datasets, but still suffers from hallucinations and under-splitting. |
Learning To Split and Rephrase From Wikipedia Edit History (D18-1)
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| Challenge: | Performing split and rephrase tasks is one of the main operations in text simplification, alongside paraphrasing and dropping less salient content. |
| Approach: | They propose to use Wikipedia's edit history to extract a rich new dataset for the task. |
| Outcome: | The proposed model scores 32 BLEU points above the previous best on the WebSplit benchmark. |
Split and Rephrase with Large Language Models (2024.acl-long)
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| Challenge: | Split and Rephrase (SPRP) tasks require modelling complex grammatical aspects to provide optimal splits and appropriate rephrasing. |
| Approach: | They evaluate large language models on the Split and Rephrase task . they show they can provide large improvements over the state of the art on main metrics . |
| Outcome: | The proposed model outperforms the state-of-the-art model on the Split and Rephrase task on the main metric, but still lacks in splitting compliance. |
Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification (N19-1)
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Reno Kriz, João Sedoc, Marianna Apidianaki, Carolina Zheng, Gaurav Kumar, Eleni Miltsakaki, Chris Callison-Burch
| Challenge: | Recent research has applied sequence-to-sequence (Seq2Sequen) models to text simplification . generic models tend to copy directly from the original sentence, resulting in outputs that are long and complex. |
| Approach: | They propose to incorporate word complexities into the loss function during training and generate a large set of diverse candidate simplifications at test time. |
| Outcome: | The proposed model can perform competitively with state-of-the-art systems while generating simpler sentences. |
We Need to Talk about Standard Splits (P19-1)
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| Challenge: | Existing methods to evaluate systems with a held-out test set are insufficient for system comparison. |
| Approach: | They propose to use multiple random splits to compare performance of systems . they replicate results on standard split but fail to reproduce some rankings . |
| Outcome: | The proposed method is based on multiple random splits to replicate results with a set of part-of-speech taggers. |
We Need to Talk About train-dev-test Splits (2021.emnlp-main)
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| Challenge: | Standard train-dev-test splits used to benchmark multiple models are now used in NLP . comparing multiple versions of the same model on the test data leads to overfitting and "expiration" of test sets. |
| Approach: | They propose to use a tune-set when developing neural network methods to do model picking. |
| Outcome: | The proposed model picker is more robust against the evaluated hyperparameter ranges than the standard split split. |
Benchmarking Long-tail Generalization with Likelihood Splits (2023.findings-eacl)
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| Challenge: | Existing methods to test out of distribution generalization have their own strengths and weaknesses. |
| Approach: | They propose a method to create challenging benchmarks that require generalizing to the tail of the distribution by re-splitting existing datasets. |
| Outcome: | The proposed approach can be customized to construct meaningful splits for a wide range of tasks. |
Fluency Boost Learning and Inference for Neural Grammatical Error Correction (P18-1)
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| Challenge: | Seq2seq models for grammatical error correction (GEC) have two limitations: (1) a seq2q model may not be well generalized with only limited error-corrected data; (2) a model may fail to completely correct a sentence with multiple errors through normal seq1sequeq inference. |
| Approach: | They propose a fluency boost learning and inference mechanism to improve the performance of seq2seq models for grammatical error correction (GEC) by generating fluency-boost sentence pairs during training. |
| Outcome: | Experiments show that the proposed model improves on both CoNLL-2014 and JFLEG benchmark datasets. |