Evaluation by Association: A Systematic Study of Quantitative Word Association Evaluation

By Anna Korhonen, Ivan Vulic, Douwe Kiela. 2017

7 citations recorded by the source corpus.

Recent work on evaluating representation learning architectures in NLP has established a need for evaluation protocols based on subconscious cognitive measures rather than manually tailored intrinsic similarity and relatedness tasks. In this work, we propose a novel evaluation framework that enables large-scale evaluation of such architectures in the free word association (WA) task, which is firmly grounded in cognitive theories of human semantic representation. This evaluation is facilitated by the existence of large manually constructed repositories of word association data. In this paper, we (1) present a detailed analysis of the new quantitative WA evaluation protocol, (2) suggest new evaluation metrics for the WA task inspired by its direct analogy with information retrieval problems, (3) evaluate various state-of-the-art representation models on this task, and (4) discuss the relationship between WA and prior evaluations of semantic representation with well-known similarity and relatedness evaluation sets. We have made the WA evaluation toolkit publicly available.

Subfields: Artificial Intelligence, Natural Language Processing, Representation & Self-Supervised Learning

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Citations in the corpus

SentEval: An Evaluation Toolkit for Universal Sentence Representations (2018, background): “On the one hand, people have measured performance on intrinsic evaluations, e.g. of human judgments of word or sentence similarity ratings (Agirre et al., 2012; Hill et al., 2016b) or of word associations (Vuli´c et al., 2017).”