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Found 3 papers, 1 papers with code
What do Transformers Know about Government?
This paper investigates what insights about linguistic features and what knowledge about the structure of natural language can be obtained from the encodings in transformer language models. In particular, we explore how BERT encodes the government relation between constituents in a sentence.
Effects of sub-word segmentation on performance of transformer language models
Lastly, we show 4. that LMs of smaller size using morphological segmentation can perform comparably to models of larger size trained with BPE -- both in terms of (1) perplexity and (3) scores on downstream tasks.
Linguistic Constructs as the Representation of the Domain Model in an Intelligent Language Tutoring System
This paper presents the development of an AI-based language learning platform Revita.
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