Language modeling is the task of predicting the next word or character in a document.
( Image credit: Exploring the Limits of Language Modeling )
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Abstract Meaning Representations (AMRs) are broad-coverage sentence-level semantic graphs.
SOTA for AMR-to-Text Generation on LDC2017T10
Long short-term memory (LSTM) networks and their variants are capable of encapsulating long-range dependencies, which is evident from their performance on a variety of linguistic tasks.
Neural language models are becoming the prevailing methodology for the tasks of query answering, text classification, disambiguation, completion and translation.
We show that this approach, which we call infilling by language modeling, can enable LMs to infill entire sentences effectively on three different domains: short stories, scientific abstracts, and lyrics.
MaskGAN opens the query for the conditional language model by filling in the blanks between the given tokens.
Automatic sentence summarization produces a shorter version of a sentence, while preserving its most important information.
As evidence, we use the latest advances in language modeling to build a single pre-trained QA model, UnifiedQA, that performs surprisingly well across 17 QA datasets spanning 4 diverse formats.