RUSSE

WiC: The Word-in-Context Dataset A reliable benchmark for the evaluation of context-sensitive word embeddings.

Depending on its context, an ambiguous word can refer to multiple, potentially unrelated, meanings. Mainstream static word embeddings, such as Word2vec and GloVe, are unable to reflect this dynamic semantic nature. Contextualised word embeddings are an attempt at addressing this limitation by computing dynamic representations for words which can adapt based on context.

Russian SuperGLUE task borrows original data from the Russe project, Word Sense Induction and Disambiguation shared task (2018)

Task Type

Reading Comprehension. Binary Classification: true/false

Example

{
  "idx" : 8,
  "word" : "дорожка",
  "sentence1" : "Бурые ковровые дорожки заглушали шаги",
  "sentence2" : "Приятели решили выпить на дорожку в местном баре",
  "start1" : 15,
  "end1" : 23,
  "start2" : 26,
  "end2" : 34,
  "label" : false,
  "gold_sense1" : 1,
  "gold_sense2" : 2
}

How did we collect data?

All text examples were collected from Russe original dataset, already collected by Russian Semantic Evaluation at ACL SIGSLAV. Human assessment was carried out on Yandex.Toloka.

In version 2, we have manually collected in the same format testset.