A Comparison of Neural Models for Word Ordering

By Eva Hasler, Felix Stahlberg, Marcus Tomalin, Adrià de Gispert, Bill Byrne. 2017

21 citations recorded by the source corpus.

We compare several language models for the word-ordering task and propose a new bag-to-sequence neural model based on attention-based sequence-to-sequence models. We evaluate the model on a large German WMT data set where it significantly outperforms existing models. We also describe a novel search strategy for LM-based word ordering and report results on the English Penn Treebank. Our best model setup outperforms prior work both in terms of speed and quality.

Subfields: Natural Language Processing

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In Natural Language Processing

1. Trung Bui
2. Anh Totti Nguyen
3. Thang M. Pham
4. Ming Yan
5. Zuyi Bao
6. Luo Si
7. Liwei Peng
8. Bin Bi

Citations in the corpus

• StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding (2019, background): “Since language fluency is determined by the ordering of words and sentences, finding the best permutation of a set of words and sentences is an essential problem in many NLP tasks, such as machine translation and NLU [9].”
• Out of Order: How important is the sequential order of words in a sentence in Natural Language Understanding tasks? (2020, background): “Thus, arranging a set of words in a correct order is considered a key problem in language modeling (Hasler et al., 2017; Zhang and Clark, 2015).”