122 citations · 161 across the 2 of their papers we have counts for
3 papers
Language Models Explain Word Reading Times Better Than Empirical Predictability
Markus J. Hofmann, Steffen Remus, Chris Biemann +2
Though there is a strong consensus that word length and frequency are the most important single-word features determining visual-orthographic access to the mental lexicon, there is…
Word Sense Disambiguation for 158 Languages using Word Embeddings Only
Varvara Logacheva, Denis Teslenko, Artem Shelmanov +7
Disambiguation of word senses in context is easy for humans, but is a major challenge for automatic approaches. Sophisticated supervised and knowledge-based models were developed t…
Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings
Gregor Wiedemann, Steffen Remus, Avi Chawla +1
Contextualized word embeddings (CWE) such as provided by ELMo (Peters et al., 2018), Flair NLP (Akbik et al., 2018), or BERT (Devlin et al., 2019) are a major recent innovation in…