Question: What are the weaknesses of their proposed interpretability quantification method?
Evidence: Our interpretability measurements are based on our proposed dataset SEMCAT, which was designed to be a comprehensive dataset that contains a diverse set of word categories. Yet, it is possible that the precise interpretability scores that are measured here are biased by the dataset used. In general, two main properties of the dataset can affect the results: category selection and within-category word selection. To examine the effects of these properties on interpretability evaluations, we create alternative datasets by varying both category selection and word selection for SEMCAT. Since SEMCAT is comprehensive in terms of the words it contains for the categories, these datasets are created by subsampling the categories and words included in SEMCAT. Since random sampling of words within a category may perturb the capacity of the dataset in reflecting human judgement, we subsample r% of the words that are closest to category centers within each category, where $r \in \lbrace 40,60,80,100\rbrace $ . To examine the importance of number of categories in the dataset we randomly select $m$ categories from SEMCAT where $m \in \lbrace 30,50,70,90,110\rbrace $ . We repeat the selection 10 times independently for each $m$ .
In contrast to the category coverage, the effects of within-category word coverage on interpretability scores can be more complex. Starting with few words within each category, increasing the number of words is expected to more uniformly sample from the word distribution, more accurately reflect the semantic relations within each category and thereby enhance interpretability scores. However, having categories over-abundant in words might inevitably weaken semantic correlations among them, reducing the discriminability of the categories and interpretability of the embedding. Table 3 shows that, interestingly, changing the category coverage has different effects on the interpretability scores of different types of embeddings. As category word coverage increases, interpretability scores for random embedding gradually decrease while they monotonically increase for the GloVe embedding. For semantic spaces $\mathcal {I}$ and $\mathcal {I}^*$ , interpretability scores increase as the category coverage increases up to 80 $\%$ of that of SEMCAT, then the scores decrease. This may be a result of having too comprehensive categories as argued earlier, implying that categories with coverage of around 80 $\%$ of SEMCAT are better suited for measuring interpretability. However, it should be noted that the change in the interpretability scores for different word coverages might be effected by non-ideal subsampling of category words. Although our word sampling method, based on words' distances to category centers, is expected to generate categories that are represented better compared to random sampling of category words, category representations might be suboptimal compared to human designed categories.
Yet, it is possible that the precise interpretability scores that are measured here are biased by the dataset used.
However, it should be noted that the change in the interpretability scores for different word coverages might be effected by non-ideal subsampling of category words. Although our word sampling method, based on words' distances to category centers, is expected to generate categories that are represented better compared to random sampling of category words, category representations might be suboptimal compared to human designed categories.
Answer: can be biased by dataset used and may generate categories which are suboptimal compared to human designed categories