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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Transformer-based language model approaches to automated story generation currently provide state-of-the-art results.
Therefore, this paper presents the first ablation study focused on Polish, which, unlike the isolating English language, is a fusional language.
Compensation of the decoder model with the probability ratio approach allows more efficient integration of an external language model, and we report 5. 9% and 11. 5% WER on the SWB and CHM parts of Hub5'00 with very simple LSTM models.
In this paper, we propose an Unsupervised Document Expansion with Generation (UDEG) framework with a pre-trained language model, which generates diverse supplementary sentences for the original document without using labels on query-document pairs for training.
Our model also outperforms the RoBERTa-Large model on several English tasks of the GLUE benchmark by 0. 3% on average while handling 99 more languages.
The evaluated baseline precision of coreference relation extraction on the corpus is 71, that is higher the results reached on other Russian corpora.
To facilitate evaluation of such metrics, we introduce the Benchmark for Evaluation of Grounded INteraction (BEGIN).
We present the Zero Resource Speech Challenge 2021, which asks participants to learn a language model directly from audio, without any text or labels.