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 )
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Additionally, these models are typically trained via maxi- mum likelihood and teacher forcing.
#3 best model for Multivariate Time Series Imputation on Basketball Players Movement
Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets.
SOTA for Language Modelling on Text8 (using extra training data)
In this paper, we present HuggingFace's Transformers library, a library for state-of-the-art NLP, making these developments available to the community by gathering state-of-the-art general-purpose pretrained models under a unified API together with an ecosystem of libraries, examples, tutorials and scripts targeting many downstream NLP tasks.
Large-scale language models show promising text generation capabilities, but users cannot easily control particular aspects of the generated text.
This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time.
In this paper, we develop Neural Assistant: a single neural network model that takes conversation history and an external knowledge source as input and jointly produces both text response and action to be taken by the system as output.
fairseq is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks.
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
We observe that our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.
In this work, we introduce a model and beam-search training scheme, based on the work of Daume III and Marcu (2005), that extends seq2seq to learn global sequence scores.
#17 best model for Machine Translation on IWSLT2015 German-English