Sparser is better: one step closer to word embedding interpretability (2023)

Abstract: Sparse word embeddings models (SPINE, SINr) are designed to embed words in interpretable dimensions. An interpretable dimension is such that a human can interpret the semantic (or syntactic) relations between words active for a dimension. These models are useful for critical downstream tasks in natural language processing (e.g. medical or legal NLP), and digital humanities applications. This work extends interpretability at the vector level with a more manageable number of activated dimensions following recommendations from psycholinguistics. Subsequently, one of the key criteria to an interpretable model is sparsity: in order to be interpretable, not every word should be represented by all the features of the model, especially if humans have to interpret these features and their relations. This raises one question: to which extent is sparsity sustainable with regard to performance? We thus introduce a sparsification procedure to evaluate its impact on two interpretable methods (SPINE and SINr) to tend towards sustainable vector interpretability. We also introduce stability as a new criterion to interpretability. Our stability evaluations show little albeit non-zero variation for SPINE and SINr embeddings. We then show that increasing sparsity does not necessarily interfere with performance. These results are encouraging and pave the way towards intrinsically interpretable word vectors.

## introduction word embeddings models @xcite @xcite allowed tremendous evolution in natural language processing. however, they embed the lexicon in dense representation spaces with opaque dimensions. it is possible to obtain an understanding of these models via probing @xcite and embedding matrix analysis @xcite . however such methods are subject to criticism with regard to the interpretation that can actually be drawn from them @xcite @xcite . this a posteriori approach to understanding models' decisions corresponds to the explainability paradigm in machine learning. on the other hand, interpretability @xcite ) is defined for word embedding models as the possibility to find semantic (or syntactic) consistency in the dimensions of the embedding space @xcite @xcite . models such as spine @xcite and sinr @xcite meet this requirement: as far as we know, only the interpretability of dimensions is considered in the literature and human evaluations such as the word intrusion detection @xcite are targeted specifically towards this aspect. in this paper, we introduce vector-level interpretability and define it as the capacity for a speaker to make sense of the set of activated dimensions in a word vector. it is possible only if the set of dimensions to describe the word is limited. the size of this set is bounded by two different kinds of psychological experiments: semantic features production @xcite and features retention @xcite . this body of literature comes to an agreement at roughly ten fea-tures. we consider in this paper that this number of features is a desirable horizon for vector-level interpretability. following this objective and to further reduce the amount of information provided to the speaker, we also consider binary word vectors as in @xcite . moreover, this binary approach is consistent with componential analysis @xcite . considering these criteria, and to tend towards more interpretability, our work offers the following contributions : to this end, we introduce section 2 the criteria for interpretability and their different settings in the literature. section 3 introduces the models considered for our experiments. in section 4, we detail the experimental setup adopted to evaluate the impact of sparsity as well as binarity on performance and vector-level interpretability. in section 5, we demonstrate that the trade-off between sparsity and interpretability is not as strong as one would think. finally, section 6 illustrates the impact of sparsity on word vectors and discusses its benefits. stability. pierrejean (2020) demonstrate the nondeterminism of neural models' training which lead to variations in evaluation scores and word neighborhoods. on the front of explicability, new deterministic methods are emerging @xcite . however, @xcite encourages to prioritise interpretable approaches over explicable approaches, motivating this work. from these observations and as stated section 1, we refine the criteria necessary to enable vectorlevel interpretability by redefining sparsity and adding stability. binary embeddings. prototypicality theory @xcite introduced the paradigm of weighted features in psychology and linguistics. however, feature-based analysis preempted this theoretical framework with componential analysis. this approach based on binary features was used by anthropological linguists @xcite , in structuralist work @xcite and in cognitively informed generativist frameworks @xcite . @xcite construct binary vectors using sparse coding to sparsify dense word embeddings in more dimensions than the original space-called overcomplete vectors (spowv). the model is then binarized simply by setting each non-zero value to one. in computer science, another use to binary models is to reduce the memory footprint of word embeddings by replacing floats with bits and also the compute needed to exploit these representations. it is especially critical in lowresource embedded systems-e.g mobile phones. @xcite and @xcite introduce autoencoder approaches to binarize ## results stability. the first property we consider with regards to interpretability is the stability of the models trained. this experiment is twofold, it allows to show whether methods are stable and also sets reference values for the similarity evaluation prior to sparsifying. each model was run ten times on the same data with the same hyperparameters. as reported in ## discussion our results show that there is not necessarily a trade-off between interpretability and performance. ## conclusion previously, the interpretability of embedding spaces focused mainly on dimension, this work redefined interpretability from the vector standpoint. we state that stability of the models and sparsity are necessary conditions to intepretability. constraining on sparsity echoes psycholinguistic plausibility, it is essential to find semantic coherence within dimension of the embedding space but also to describe a word with a limited set of these dimensions. we hypothesize that vectors constrained following this protocol are interpretable by a speaker, since it becomes possible to manipulate this small number of dimensions in working memory. interpretable word embedding models achieve good results on the intrinsic word similarity evaluation task even with higher sparseness levels. sinr even benefits from being sparsified. furthermore, we show through examples that dimensions remain interpretable even on sparsified vectors and that, indeed words that are close in the embedding space are represented by a common set of dimensions. lastly, we show that real-valued vectors are a slight improvement upon binary representation. these results allow to reconsider the interpretability performance for distributed representations. a following step would be to conceive an evaluation framework to measure vector-level interpretability, allowing us to investigate if and how speakers would make sense of interpretable word vectors. such models also open up new perspectives in which theoretical models describing the lexicon benefit from semantic features of word embeddings. in the field of semantic drift detection, it would also allow to easily characterize the drift by keeping track of the few dimensions at stake.