text: 445,15,1034711166835216387,319518346,Jose Camacho-Collados,"Humans are very good at distinguishing senses given different contexts but computers (including SotA models like #ELMo, sense embeddings...) still struggle with it. Check out our new dataset on this topic! #NLProc Dataset: <LINK> Paper: <LINK> <LINK> With @tpilehvar @yogarshi Thank you for the pointer, very interesting! And yes, not sure whether the conclusions of these studies are actually very positive... 😕 But at least they should encourage further research on modeling meaning in context, as there definitely seems to be room for improvement",https://arxiv.org/abs/1808.09121,"By design, word embeddings are unable to model the dynamic nature of words' semantics, i.e., the property of words to correspond to potentially different meanings. To address this limitation, dozens of specialized meaning representation techniques such as sense or contextualized embeddings have been proposed. However, despite the popularity of research on this topic, very few evaluation benchmarks exist that specifically focus on the dynamic semantics of words. In this paper we show that existing models have surpassed the performance ceiling of the standard evaluation dataset for the purpose, i.e., Stanford Contextual Word Similarity, and highlight its shortcomings. To address the lack of a suitable benchmark, we put forward a large-scale Word in Context dataset, called WiC, based on annotations curated by experts, for generic evaluation of context-sensitive representations. WiC is released in this https URL ","WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive