Text generation is the task of generating text with the goal of appearing indistinguishable to human-written text.
( Image credit: Adversarial Ranking for Language Generation )
|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
We consider the structured data record input as a set of RDF entity-relation triples, a format widely used for knowledge representation and semantics description.
It is important to define meaningful and interpretable automatic evaluation metrics for open-domain dialog research.
Generating inferential texts about an event in different perspectives requires reasoning over different contexts that the event occurs.
This approach allows us to explore both spaces of natural language and formal representations, and facilitates information sharing through the latent space to eventually benefit NLU and NLG.
The two dominant approaches to neural text generation are fully autoregressive models, using serial beam search decoding, and non-autoregressive models, using parallel decoding with no output dependencies.
In this work, we propose a method for situating QA responses within a SEQ2SEQ NLG approach to generate fluent grammatical answer responses while maintaining correctness.
In these methods, syntactic-guidance is sourced from a separate exemplar sentence.
Meaning Representations (AMRs) are broad-coverage sentence-level semantic graphs.
Ranked #2 on AMR-to-Text Generation on LDC2017T10
We present MixingBoard, a platform for quickly building demos with a focus on knowledge grounded stylized text generation.
MaskGAN opens the query for the conditional language model by filling in the blanks between the given tokens.