[PDF] Top 20 Large Scale Transfer Learning for Natural Language Generation

... In the second adaptation scheme, the multi- input model, the pretrained language model is duplicated in an encoder-decoder architecture (Fig. 1b). Similar to the single-input model, natu- ral separators, ... See full document

... dataset [Toutanova et al.2015] contains alignments of the Clueweb dataset with Freebase-named entities [Gabrilovich et al.2013] and Freebase triples. The dataset is of rela- tively large size (2.7 million ... See full document

... to scale to SNLI’s size without modification, so a more com- plete comparison of approaches will have to wait for future ...could scale readily: (i) models from a well-known NLI system, the Excitement Open ... See full document

... stylistic transfer is the difficulty in using linguistic features to signal a certain ...would transfer this knowledge into controlling realization decisions in an NLG sys- ...the large number of ... See full document

... In this work we explore the applicability of current NLG models for task-oriented dialog, based on a MR-to-text framework using Encoder- Decoder architectures, to open-domain QA. This allows us to investigate the ... See full document

... in language make the re- sponse rather ...in natural language gen- eration (Wen et ...deep learning technology in natural language processing increases these models’ ca- pacity ... See full document

... titative study on the discourse of counseling con- versations. We developed a set of novel computa- tional discourse analysis methods suited for large- scale datasets and used them to discover actionable ... See full document

... ICONOCLAST was originally developed as a component of a Natural Language Generation system. It assumes that the propositional content of the desired text is already formally encoded, along with a ... See full document

... point scale: clarity, useful- ness, repetitiveness, and whether it ever misled them (the scale is appropriately arranged: the highest clar- ity but the lowest repetitiveness receive 5 ... See full document

... massive scale social interaction has the potential to yield new knowledge about the inner-workings of interaction in such environments so that support for healthy community formation can be designed and ...to ... See full document

... a learning scheme which provides the ability to jointly learn two mod- els for NLG and for NLU using large amount of unannotated data and small amount of anno- tated ...of large annotated data source ... See full document

... Prior work on CLTL for SLU has mainly focused on using machine translation (e.g. Garc´ıa et al. (2012); He et al. (2013); Gaspers et al. (2018)). Until recently, few approaches based on cross- lingual joint training and ... See full document

... Combining Multiple, Large Scale Resources in a Reusable Lexicon for Natural Language Generation C o m b i n i n g M u l t i p l e , L a r g e S c a l e R e s o u r c e s in a R e u s a b l e L e x i c[.] ... See full document

... The resulting lexicon contains syntactic, semantic, and lexical knowledge, indexed by senses of words as required by generation, including: A complete list of syntactic subcategorization[r] ... See full document

... AMR meaning bank provides a large new corpus that, for the first time, enables us to study the problem of grammar induction for broad-coverage semantic parsing. However, it also presents sig- nificant challenges ... See full document

... curriculum learning strategy in all of our LSTM models (with and without the output peephole con- nection) that starts training on sentences of length 5 at the word level and 13 for characters and in- creases the ... See full document

... to natural language understand- ...a large collection of syntactic para- phrase pairs, and introduce an adaptation scheme that allows us to tackle a variety of text transformation tasks via ...from ... See full document

... Time expressions present a number of challenges for language understanding systems. They have rich, compositional structure (e.g., “2nd Friday of July”), can be easily confused with non-temporal phrases (e.g., the ... See full document

... There is growing research interest in automatic sentence generation (Vinyals et al., 2015; Rush et al., 2015; Sordoni et al., 2015). Coincidentally (or inevitably), media companies have increas- ingly attempted to ... See full document

... via natural language are in their ...creating natural lan- guage generation components that can produce quality output ...a natural language generation system that is ... See full document