Discriminative Syntax-Based Word Ordering for Text Generation

By Yue Zhang, Stephen Clark. 2015

33 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding (2019, methodology): “Part of larger discussion as to whether LSTMs are capturing syntactic phenomena linearization, is standardized in a recent line of research as a method useful for isolating the performance of text-to-text generation [16] models.”
CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning (2020, background): “Two related scenarios with our task is lexically constrained decoding and word ordering (Zhang and Clark, 2015; Hasler et al., 2018; Dinu et al., 2019; Hokamp and Liu, 2017; Puduppully et al., 2017; Miao et al., 2019).”
A Comparison of Neural Models for Word Ordering (2017, background): “Word ordering has often been addressed as syntactic linearization which is a strategy that involves using syntactic structures or partof-speech and dependency labels (Zhang and Clark, 2011; Zhang et al., 2012; Zhang and Clark, 2015; Liu et al., 2015; Puduppully et al., 2016).”
Neural Sentence Ordering (2016, background): “Comparing with word ordering (Tillmann and Ney, 2000; Zhang et al., 2012; Zhang and Clark, 2015; Schmaltz et al., 2016), sentence ordering is still less studied.”
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).”