Evaluating a Multi-sense Definition Generation Model for Multiple Languages

Kabiri, Arman; Cook, Paul F.

doi:10.1007/978-3-030-58323-1_16

Cited by 6 publications

(9 citation statements)

References 9 publications

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“…Noraset et al (2017) report BLEU of 31 and 23, depending on the dictionary. Subsequent experiments report, for the same approach, BLEU scores that range from as little as 11 (Gadetsky et al, 2018) to as much as 60 (Kabiri and Cook, 2020). The variation can be great even for the same language and experimental setup (Kabiri and Cook, 2020).…”

Section: Related Workmentioning

confidence: 93%

“…Subsequent work on Definition Modeling focused on variations of the problem of prediction of a word gloss from the word sense. These approaches consider gloss prediction based on sensespecific word embeddings (Gadetsky et al, 2018;Kabiri and Cook, 2020;Zhu et al, 2019), and on a word-based context indicating the word sense (Bevilacqua et al, 2020;Gadetsky et al, 2018;Mickus et al, 2019;Yang et al, 2020;. The proposed approaches are based either on RNNs (Gadetsky et al, 2018;Kabiri and Cook, 2020;Zhu et al, 2019) or Transformers (Bevilacqua et al, 2020;Mickus et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

“…These approaches consider gloss prediction based on sensespecific word embeddings (Gadetsky et al, 2018;Kabiri and Cook, 2020;Zhu et al, 2019), and on a word-based context indicating the word sense (Bevilacqua et al, 2020;Gadetsky et al, 2018;Mickus et al, 2019;Yang et al, 2020;. The proposed approaches are based either on RNNs (Gadetsky et al, 2018;Kabiri and Cook, 2020;Zhu et al, 2019) or Transformers (Bevilacqua et al, 2020;Mickus et al, 2019). All of the previous approaches rely on word embeddings pre-trained on large corpora, most commonly word2vec (Mikolov et al, 2013).…”

Section: Related Workmentioning

confidence: 99%

“…Sense-aware approaches that take embeddings as input make use of either sense-aware word embeddings (Gadetsky et al, 2018;Kabiri and Cook, 2020) or of decomposition of word embeddings into sense-specific vectors (Zhu et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

“…The variation can be great even for the same language and experimental setup (Kabiri and Cook, 2020). The original approach of Noraset et al (2017) remains competitive in the sense-aware setting, with the sense-aware approaches achieving BLEU increases that range between 1 − 2 (Gadetsky et al, 2018;Kabiri and Cook, 2020; and 5 − 6 (Kabiri and Cook, 2020;Yang et al, 2020;, depending on the setting.…”

Section: Related Workmentioning

confidence: 99%

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IRB-NLP at SemEval-2022 Task 1: Exploring the Relationship Between Words and Their Semantic Representations

Korenčić¹,

Grubišić²

2022

Preprint

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What is the relation between a word and its description, or a word and its embedding? Both descriptions and embeddings are semantic representations of words. But, what information from the original word remains in these representations? Or more importantly, which information about a word do these two representations share? Definition Modeling and Reverse Dictionary are two opposite learning tasks that address these questions. The goal of the Definition Modeling task is to investigate the power of information laying inside a word embedding to express the meaning of the word in a humanly understandable way -as a dictionary definition. Conversely, the Reverse Dictionary task explores the ability to predict word embeddings directly from its definition. In this paper, by tackling these two tasks, we are exploring the relationship between words and their semantic representations. We present our findings based on the descriptive, exploratory, and predictive data analysis conducted on the CODWOE dataset. We give a detailed overview of the systems that we designed for Definition Modeling and Reverse Dictionary tasks, and that achieved top scores on SemEval-2022 CODWOE challenge in several subtasks. We hope that our experimental results concerning the predictive models and the data analyses we provide will prove useful in future explorations of word representations and their relationships.

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Section: Related Workmentioning

confidence: 93%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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IRB-NLP at SemEval-2022 Task 1: Exploring the Relationship Between Words and Their Semantic Representations

Korenčić¹,

Grubišić²

2022

Preprint

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Exploring the Limits of Large Language Models for Word Definition Generation: A Comparative Analysis

Rodríguez-Betancourt,

Casasola-Murillo

2023

2023 XLIX Latin American Computer Conference (CLEI)

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Semeval-2022 Task 1: CODWOE – Comparing Dictionaries and Word Embeddings

Mickus¹,

Deemter²,

Constant³

et al. 2022

Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

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Word embeddings have advanced the state of the art in NLP across numerous tasks. Understanding the contents of dense neural representations is of utmost interest to the computational semantics community. We propose to focus on relating these opaque word vectors with human-readable definitions, as found in dictionaries. This problem naturally divides into two subtasks: converting definitions into embeddings, and converting embeddings into definitions. This task was conducted in a multilingual setting, using comparable sets of embeddings trained homogeneously.

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Evaluating a Multi-sense Definition Generation Model for Multiple Languages

Cited by 6 publications

References 9 publications

IRB-NLP at SemEval-2022 Task 1: Exploring the Relationship Between Words and Their Semantic Representations

IRB-NLP at SemEval-2022 Task 1: Exploring the Relationship Between Words and Their Semantic Representations

Exploring the Limits of Large Language Models for Word Definition Generation: A Comparative Analysis

Semeval-2022 Task 1: CODWOE – Comparing Dictionaries and Word Embeddings

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