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Found 5 papers, 4 papers with code
Linguistic Knowledge Can Enhance Encoder-Decoder Models (If You Let It)
In this paper, we explore the impact of augmenting pre-trained Encoder-Decoder models, specifically T5, with linguistic knowledge for the prediction of a target task.
Outliers Dimensions that Disrupt Transformers Are Driven by Frequency
While Transformer-based language models are generally very robust to pruning, there is the recently discovered outlier phenomenon: disabling only 48 out of 110M parameters in BERT-base drops its performance by nearly 30% on MNLI.
On the interaction of automatic evaluation and task framing in headline style transfer
An ongoing debate in the NLG community concerns the best way to evaluate systems, with human evaluation often being considered the most reliable method, compared to corpus-based metrics.
Linguistic Profiling of a Neural Language Model
In this paper we investigate the linguistic knowledge learned by a Neural Language Model (NLM) before and after a fine-tuning process and how this knowledge affects its predictions during several classification problems.
GePpeTto Carves Italian into a Language Model
We provide a thorough analysis of GePpeTto's quality by means of both an automatic and a human-based evaluation.
