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Rare word representation has recently enjoyed a surge of interest, owing tothe crucial role that effective handling of infrequent words can play inaccurate semantic understanding. However, there is a paucity of reliablebenchmarks for evaluation and comparison of these techniques. We show in thispaper that the only existing benchmark (the Stanford Rare Word dataset) suffersfrom low-confidence annotations and limited vocabulary; hence, it does notconstitute a solid comparison framework. In order to fill this evaluation gap,we propose CAmbridge Rare word Dataset (Card-660), an expert-annotated wordsimilarity dataset which provides a highly reliable, yet challenging, benchmarkfor rare word representation techniques. Through a set of experiments we showthat even the best mainstream word embeddings, with millions of words in theirvocabularies, are unable to achieve performances higher than 0.43 (Pearsoncorrelation) on the dataset, compared to a human-level upperbound of 0.90. Werelease the dataset and the annotation materials athttps://pilehvar.github.io/card-660/.

Card-660: Cambridge Rare Word Dataset - a Reliable Benchmark for Infrequent Word Representation Models