Assessing the Limits of the Distributional Hypothesis in Semantic Spaces: Trait-based Relational Knowledge and the Impact of Co-occurrences (2022)

Abstract: The increase in performance in NLP due to the prevalence of distributional models and deep learning has brought with it a reciprocal decrease in interpretability. This has spurred a focus on what neural networks learn about natural language with less of a focus on how. Some work has focused on the data used to develop data-driven models, but typically this line of work aims to highlight issues with the data, e.g. highlighting and offsetting harmful biases. This work contributes to the relatively untrodden path of what is required in data for models to capture meaningful representations of natural language. This is entails evaluating how well English and Spanish semantic spaces capture a particular type of relational knowledge, namely the traits associated with concepts (e.g. bananas-yellow), and exploring the role of co-occurrences in this context.

## introduction vector space models have been the main driving force behind progress in nlp. most work in this area, either in the form of static or contextualised embeddings, has been based on co-occurrence statistics and largely driven by the distributional hypothesis @xcite . this has also resulted in these representations seemingly capturing certain relational knowledge, such as word analogies @xcite . in this context, @xcite found that the ability of word embeddings to evaluate analogies was not greatly impaired by removing co-occurrences related to relational pairs. this suggests there are limits to how the distributional hypothesis impacts the encoding of relational knowledge. we extend this line of work by focusing on the relational knowledge of concepts and traits. we also creep beyond english by translating concept and traits used in one of our datasets into spanish. contributions: (1) we show that there is no impact on the ability of semantic spaces to predict whether a pair of embeddings corresponds to a trait-concept pair or to predict what traits a given concept has when removing co-occurrences of concepts and traits. (2) we developed a freely available dataset that can be used for further trait-based relational knowledge analyses for english and spanish. 1 2 related work what models learn evaluation of neural semantic spaces has focused on what knowledge they capture with a slew of work showing that some knowledge of analogies can be seen by applying simple transformations @xcite @xcite @xcite . others have investigated what syntactic information neural semantic spaces seem to capture with most showing that they do capture something deeper than surface patters @xcite @xcite . however, they fail to exhaustively capture syntactic phenomena and specifically have been shown to struggle with polarity @xcite and certain filler-gap dependencies @xcite . pretrained language models (plms) have been found to capture varying degrees of syntactic information @xcite @xcite , however, they have also been shown to struggle to predict the grammaticality of sentences @xcite and seem to depend on fragile heuristics rather than anything deeper @xcite . relational knowledge more specifically with respect to relational knowledge and semantic spaces, for some time now work has shown that semantic spaces could encode certain relational knowledge, e.g. knowledge of the relative positioning of geographical locations @xcite . similarly, @xcite found that embeddings capture something of relational knowledge associated with countries and cities, e.g. how countries related to one another with respect to gdp. @xcite found that word embeddings captured some taxonomic relational knowledge but fared less well with respect to trait-based relational knowledge. often analogy completion tasks are used to investigate what sort of relational knowledge a semantic space has captured with early work showing that simple linear transformations were enough to highlight analogies @xcite . this method has drawn some criticism and has been challenged as a robust means of evaluating what relational knowledge models capture @xcite @xcite . attempts to evaluate what plms capture of relational knowledge have also been made, highlighting that these larger, more data-hungry models capture some but not all relational knowledge @xcite . patterns in data however, all the work cited above focuses work focuses on what models learn about relational knowledge and not how, or rather what are the salient signals in the data used in these techniques that manifest in relational knowledge. some work has been done in this direction, with @xcite showing co-occurrences are not necessary in their distributional model of courses to predict similar or related courses. @xcite evaluated this finding in neural semantic spaces, finding that the ability of a semantic space to complete analogies isn't impacted when removing co-occurrences it is important to understand what aspects of the data result in what models learn because without this semblance of interpretability, problematic biases can creep in, e.g. gender biases in word2vec @xcite or in bert @xcite . attempts have been made to mitigate certain biases in contexualised word embeddings @xcite , but in order to do so, the biases have to be known. also, shwartz and choi (2020) discuss the issue of reporting bias in the data typically used in nlp, where rarer occurrences are more likely to be explicitly mentioned than common ones which results in models that can generalise about under-reported phenomena but not temper the over-reported information. therefore it is necessary to understand the nature of the data and how it impacts what models capture and how. in this work, we aim to expand on the work of @xcite in two main ways. first, we do not use analogies and analogy completion to evaluate the impact co-occurrences of concepttraits has on relational knowledge developed in neural semantic spaces, but instead use a dataset of different trait-based relations (e.g. is-colour, has-component) derived from the mcrae and norms feature datasets. this allows us to more directly evaluate the ability of models to predict relational knowledge by casting the evaluation as a simple classification task (both in a multi class and binary class setting). and second, we extend the analysis by looking at spanish data as well to evaluate whether the results extend beyond english. ## methodology the methodology follows five sequential steps: the development of datasets that include concepts and their traits (section 3.1); the selection and processing of large general-domain corpora (section 3.2); the transformation of the selected corpora based on the concept-trait datasets to test our hypothesis (section 3.3); training of word embeddings on the original and adapted corpora (section 3.4); and finally the evaluation of the embeddings based on the trait-based datasets (section 3.5). ## discussion the results highlight some tentatively interesting patterns with respect to trait types. in both english and spanish, models perform consistently well on component traits, although for norms this turned out to be only over 2 traits, effectively casting it as a binary classification. materials is the next consistently highest performing trait type across corpora and language with size & shape and tactile not far behind for english, but with a bigger gap in spanish. the performance on colour traits is low across all settings and languages. this doesn't appear to be based on the size of the trait subset, e.g. the component subset is one of the smaller sets, yet has high performance and the performance of the other trait types don't vary with respect to the number of instance and unique features. the number of removed sentences, as shown in tables 3 and 4 , gives a vague indication of the occurrences of the concepts in the dataset and the occurrence of their traits with colour sentence removals being the second highest for mcrae-en across all three english corpora, the third highest for norms-en, and the highest for mcrae-es across all spanish corpora. these rankings are consistent across extraction methods. therefore, it is unlikely that the embeddings for the colours and the corresponding concepts (often concepts that occur in the other datasets) are somehow low quality due to low occurrences of these words. more likely is that the colour relation is more difficult than the other trait types as the other types are more tangible and more specific. although this doesn't necessarily hold for size & shape traits, specifically sizes which tend to be relative, e.g. in mcrae a plane can be large (which it is, relative to most things) but so too can a bathtub (which it is, relative to a mouse or other such timorous beasties, but not relative to a house). however, size & shape is consistently one of the traits that models perform worst on especially with norms-en and mcrae-es. as a final note, the different extraction methods yield no differences when compared to one another. this can be observed clearly in figure 3 in the main text and figure 6 in appendix b. while the number of extracted instances using the syntactically related co-occurrences is very low and so difficult to draw any major conclusions, the num-ber of sentence-based and window-based instances removed are quite high and are similar in magnitude. from this, we can deduce that the proximity of the words also doesn't have a major impact on the ability of a semantic space to encode relational knowledge. it could still be the case that if the data used to train models contained more syntactically related concept-trait pairs, they would encode more relational knowledge, but it is clear that their absence doesn't result in the models losing what relational knowledge they can capture. many questions remain on how these distributional models encode relational knowledge. we have merely presented results which do not support the hypothesis that direct co-occurrence are the major signal for this process as related to trait-based relational knowledge. language models and wider impact of findings. whether the results observed here for static embeddings would hold for plms isn't a given. while they are still based on the same distributional hypothesis and adopt statistical methods to encode salient features of language, they could potentially be more sensitive to the loss of co-occurrences in the training data. but this is an open research question that requires specific experimentation which has its own difficulties, i.e. prompting language models often includes lexical clues which cloud our ability to say with any great certainty if they have captured some phenomenon or not, see kassner and schütze (2020) for sensitivity of plms to mispriming). the results do suggest that merely increasing the amount of data used likely won't result in any major improvements in the ability of models to encode relational knowledge or commonsense knowledge more generally, which is attested to by recent work in @xcite . potentially, we need to look to more complex methods to augment nlp systems with commonsense knowledge potentially using multimodal systems, e.g. language models trained with visual cues as was done in @xcite to offset reporting bias with respect to colours. alternatively, we can focus on the linguistic input and consider how to add stronger signals in the data used to train nlp systems. ## conclusion we have contributed to the emerging interest in how neural semantic models encode linguistic information, focusing on trait-based relational knowl-edge. we have extended findings which showed that co-occurrences of relational pairs didn't have a major impact on a model's ability to encode knowledge of analogies by complementing this analysis with an evaluation of trait-based relational knowledge. we extended the analysis to include different extraction methods to evaluate whether a more fine-grained approach would highlight any differences in performance and found that this is not the case. the work presented here also expands beyond english and includes results in spanish which follow the same trend. finally, we have cultivated a set of datasets for different trait types in both english and spanish (based on mcrae and norms) which are available at https://github. com/cardiffnlp/trait-concept-datasets . ekaterina vylomova, laura rimell, trevor cohn, and timothy baldwin. 2016. take and took, gaggle and goose, book and read: evaluating the utility of vector differences for lexical relation learning. in proceedings of the 54th annual meeting of the association for computational linguistics (volume 1: long papers), pages 1671-1682, berlin, germany. association for computational linguistics. alex warstadt, amanpreet singh, and samuel r. bowman. 2019. neural network acceptability judgments. transactions of the association for computational linguistics, 7:625-641. ethan wilcox, roger levy, takashi morita, and richard futrell. 2018. what do rnn language models learn about filler-gap dependencies? in proceedings of the 2018 emnlp workshop blackboxnlp: analyzing and interpreting neural networks for nlp, pages 211-221.