SemEval-2018 Task 10: Capturing Discriminative Attributes

Krebs, Alicia; Lenci, Alessandro; Paperno, Denis

doi:10.18653/v1/s18-1117

Cited by 26 publications

(34 citation statements)

References 29 publications

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“…Task. The Capturing Discriminative Attributes task (Krebs et al [14]) was introduced at SemEval 2018. The aim of this task is to identify whether an attribute could help discriminate between two concepts.…”

Section: Capturing Discriminative Attributesmentioning

confidence: 99%

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The POLAR Framework: Polar Opposites Enable Interpretability of Pre-Trained Word Embeddings

Mathew

Sikdar

Lemmerich

et al. 2020

Proceedings of the Web Conference 2020

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We introduce 'POLAR' -a framework that adds interpretability to pre-trained word embeddings via the adoption of semantic differentials. Semantic differentials are a psychometric construct for measuring the semantics of a word by analysing its position on a scale between two polar opposites (e.g., cold -hot, soft -hard). The core idea of our approach is to transform existing, pre-trained word embeddings via semantic differentials to a new "polar" space with interpretable dimensions defined by such polar opposites. Our framework also allows for selecting the most discriminative dimensions from a set of polar dimensions provided by an oracle, i.e., an external source. We demonstrate the effectiveness of our framework by deploying it to various downstream tasks, in which our interpretable word embeddings achieve a performance that is comparable to the original word embeddings. We also show that the interpretable dimensions selected by our framework align with human judgement. Together, these results demonstrate that interpretability can be added to word embeddings without compromising performance. Our work is relevant for researchers and engineers interested in interpreting pre-trained word embeddings. CCS CONCEPTS• Computing methodologies → Machine learning approaches. KEYWORDS word embeddings, neural networks, interpretable, semantic differential ACM Reference Format:

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Section: Capturing Discriminative Attributesmentioning

confidence: 99%

“…Method. We used the unsupervised distributed vector cosine baseline as suggested in Krebs et al [14]. The main idea is that the discriminative attribute should be close to the word it characterizes and farther from the other concept.…”

Section: Capturing Discriminative Attributesmentioning

confidence: 99%

The POLAR Framework: Polar Opposites Enable Interpretability of Pre-Trained Word Embeddings

Mathew

Sikdar

Lemmerich

et al. 2020

Proceedings of the Web Conference 2020

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“…The dataset in the sharedtask2018 (Krebs and Paperno, 2018) is divided into three sets namely train test and validation. The training set contains automatically generated examples which are not manually curated.…”

Section: Datasetmentioning

confidence: 99%

AmritaNLP at SemEval-2018 Task 10: Capturing discriminative attributes using convolution neural network over global vector representation.

Vinayan

M²

2018

Proceedings of the 12th International Workshop on Semantic Evaluation

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The "Capturing Discriminative Attributes" sharedtask is the tenth task, conjoint with SemEval2018. The task is to predict if a word can capture distinguishing attributes of one word from another. We use GloVe word embedding, pre-trained on openly sourced corpus for this task. A base representation is initially established over varied dimensions. These representations are evaluated based on validation scores over two models, first on an SVM based classifier and second on a one dimension CNN model. The scores are used to further develop the representation with vector combinations, by considering various distance measures. These measures correspond to offset vectors which are concatenated as features, mainly to improve upon the F1score, with the best accuracy. The features are then further tuned on the validation scores, to achieve highest F1score. Our evaluation narrowed down to two representations, classified on CNN models, having a total dimension length of 1204 & 1203 for the final submissions. Of the two, the latter feature representation delivered our best F1score of 0.658024 (as per result 1 .)

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“…The goal of the shared task on Capturing Discriminative Attributes (Krebs et al, 2018) is to detect semantic difference between pairs of concepts, or in other words, determine whether a semantic property differentiates between two possibly related concepts. For example both 'bear' and 'goat' are animals, but only a 'bear' has 'claws'.…”

Section: Task and Data Descriptionmentioning

confidence: 99%

GHH at SemEval-2018 Task 10: Discovering Discriminative Attributes in Distributional Semantics

Attia

Samih

Faruqui

et al. 2018

Proceedings of the 12th International Workshop on Semantic Evaluation

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This paper describes our system submission to the SemEval 2018 Task 10 on Capturing Discriminative Attributes. Given two concepts and an attribute, the task is to determine whether the attribute is semantically related to one concept and not the other. In this work we assume that discriminative attributes can be detected by discovering the association (or lack of association) between a pair of words. The hypothesis we test in this contribution is whether the semantic difference between two pairs of concepts can be treated in terms of measuring the distance between words in a vector space, or can simply be obtained as a by-product of word co-occurrence counts.

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SemEval-2018 Task 10: Capturing Discriminative Attributes

Cited by 26 publications

References 29 publications

The POLAR Framework: Polar Opposites Enable Interpretability of Pre-Trained Word Embeddings

The POLAR Framework: Polar Opposites Enable Interpretability of Pre-Trained Word Embeddings

AmritaNLP at SemEval-2018 Task 10: Capturing discriminative attributes using convolution neural network over global vector representation.

GHH at SemEval-2018 Task 10: Discovering Discriminative Attributes in Distributional Semantics

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