Browsing by Subject "Interpretability"

Now showing 1 - 4 of 4

Open Access
Imparting interpretability to word embeddings while preserving semantic structure
(Cambridge University Press, 2020) Şenel, L. K.; Utlu, İhsan; Şahinuç, Furkan; Özaktaş, Haldun M.; Koç, Aykut
As a ubiquitous method in natural language processing, word embeddings are extensively employed to map semantic properties of words into a dense vector representation. They capture semantic and syntactic relations among words, but the vectors corresponding to the words are only meaningful relative to each other. Neither the vector nor its dimensions have any absolute, interpretable meaning. We introduce an additive modification to the objective function of the embedding learning algorithm that encourages the embedding vectors of words that are semantically related to a predefined concept to take larger values along a specified dimension, while leaving the original semantic learning mechanism mostly unaffected. In other words, we align words that are already determined to be related, along predefined concepts. Therefore, we impart interpretability to the word embedding by assigning meaning to its vector dimensions. The predefined concepts are derived from an external lexical resource, which in this paper is chosen as Roget’s Thesaurus. We observe that alignment along the chosen concepts is not limited to words in the thesaurus and extends to other related words as well. We quantify the extent of interpretability and assignment of meaning from our experimental results. Manual human evaluation results have also been presented to further verify that the proposed method increases interpretability. We also demonstrate the preservation of semantic coherence of the resulting vector space using word-analogy/word-similarity tests and a downstream task. These tests show that the interpretability-imparted word embeddings that are obtained by the proposed framework do not sacrifice performances in common benchmark tests.
Open Access
Learning interpretable word embeddings via bidirectional alignment of dimensions with semantic concepts
(Elsevier Ltd, 2022-03-22) Şenel, L. K.; Şahinuç, Furkan; Yücesoy, V.; Schütze, H.; Çukur, Tolga; Koç, Aykut
We propose bidirectional imparting or BiImp, a generalized method for aligning embedding dimensions with concepts during the embedding learning phase. While preserving the semantic structure of the embedding space, BiImp makes dimensions interpretable, which has a critical role in deciphering the black-box behavior of word embeddings. BiImp separately utilizes both directions of a vector space dimension: each direction can be assigned to a different concept. This increases the number of concepts that can be represented in the embedding space. Our experimental results demonstrate the interpretability of BiImp embeddings without making compromises on the semantic task performance. We also use BiImp to reduce gender bias in word embeddings by encoding gender-opposite concepts (e.g., male–female) in a single embedding dimension. These results highlight the potential of BiImp in reducing biases and stereotypes present in word embeddings. Furthermore, task or domain-specific interpretable word embeddings can be obtained by adjusting the corresponding word groups in embedding dimensions according to task or domain. As a result, BiImp offers wide liberty in studying word embeddings without any further effort.
Open Access
Measuring and improving interpretability of word embeddings using lexical resources
(2019-08) Şenel, Lütfi Kerem
As an ubiquitous method in natural language processing, word embeddings are extensively employed to map semantic properties of words into a dense vector representations. They have become increasingly popular due to their state-of-the-art performances in many natural language processing (NLP) tasks. Word embeddings are substantially successful in capturing semantic relations among words, so a meaningful semantic structure must be present in the respective vector spaces. However, in many cases, this semantic structure is broadly and heterogeneously distributed across the embedding dimensions. In other words, vectors corresponding to the words are only meaningful relative to each other. Neither the vector nor its dimensions have any absolute meaning, making interpretation of dimensions a big challenge. We propose a statistical method to uncover the underlying latent semantic structure in the dense word embeddings. To perform our analysis, we introduce a new dataset (SEMCAT) that contains more than 6,500 words semantically grouped under 110 categories. We further propose a method to quantify the interpretability of the word embeddings that is a practical alternative to the classical word intrusion test that requires human intervention. Moreover, in order to improve the interpretability of word embeddings while leaving the original semantic learning mechanism mostly una ected, we introduce an additive modifi- cation to the objective function of the embedding learning algorithm, GloVe, that promotes the vectors of words that are semantically related to a predefined concept to take larger values along a specified dimension. We use Roget's Thesaurus to extract concept groups and align the words in these groups with embedding dimensions using modified objective function. By performing detailed evaluations, we show that proposed method improves interpretability drastically while preserving the semantic structure. We also demonstrate that imparting method with suitable concept groups can be used to significantly improve performance on benchmark tests and to measure and reduce gender bias present in the word embeddings.
Open Access
Semantic structure and interpretability of word embeddings
(Institute of Electrical and Electronics Engineers, 2018) Şenel, Lütfi Kerem; Utlu, İhsan; Yücesoy, Veysel; Koç, Aykut; Çukur, Tolga
Dense word embeddings, which encode meanings of words to low-dimensional vector spaces, have become very popular in natural language processing (NLP) research due to their state-of-the-art performances in many NLP tasks. Word embeddings are substantially successful in capturing semantic relations among words, so a meaningful semantic structure must be present in the respective vector spaces. However, in many cases, this semantic structure is broadly and heterogeneously distributed across the embedding dimensions making interpretation of dimensions a big challenge. In this study, we propose a statistical method to uncover the underlying latent semantic structure in the dense word embeddings. To perform our analysis, we introduce a new dataset (SEMCAT) that contains more than 6500 words semantically grouped under 110 categories. We further propose a method to quantify the interpretability of the word embeddings. The proposed method is a practical alternative to the classical word intrusion test that requires human intervention.