PREDICT: Persian Reverse Dictionary

Arman, Malekzadeh,; Gheibi, Amin; Mohades, Ali

doi:10.48550/arxiv.2105.00309

Cited by 5 publications

(5 citation statements)

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“…(Yan et al, 2020) endeavored to integrate pre-trained models like BERT for cross-lingual capabilities. The most recent advancements, such as the Persian reverse dictionary by (Malekzadeh et al, 2021), maintain the momentum of NLP innovations in this realm. This evolution culminates in the CODWOE shared task's interest, which emphasizes the reconstruction of word embeddings from their definitions, a premise intimately linked to prior works.…”

Section: Reverse Dictionarymentioning

confidence: 99%

Abed at KSAA-RD Shared Task: Enhancing Arabic Word Embedding with Modified BERT Multilingual

Qaddoumi

2023

Proceedings of ArabicNLP 2023

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This paper presents a novel approach to the Arabic Reverse Dictionary Shared Task at Ara-bicNLP 2023 by leveraging the Bidirectional Encoder Representations from Transformers (BERT) Multilingual model and introducing modifications augmentation and using a multi attention head. The proposed method aims to enhance the performance of the model in understanding and generating word embeddings for Arabic definitions, both in monolingual and cross-lingual contexts. It achieved good results compared to benchmark and other models in the shared task 1 and 2.

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Section: Reverse Dictionarymentioning

confidence: 99%

Abed at KSAA-RD Shared Task: Enhancing Arabic Word Embedding with Modified BERT Multilingual

Qaddoumi

2023

Proceedings of ArabicNLP 2023

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“…In (Hedderich et al, 2019), they used attention mechanisms to integrate multi-sense embedding using LSTM and contextual word embedding (Bidirectional Encoder Representations from Transformers 1 https://www.onelook.com/thesaurus/ (BERT) to enhance performance in the reverse dictionary task. As for (Malekzadeh et al, 2021), they utilised different models to simulate the functionality of a reverse dictionary. These included a Bag of Words (BOW) model, an RNN model with additive attention, and a BiLSTM model.…”

Section: Related Workmentioning

confidence: 99%

Qamosy at Arabic Reverse Dictionary shared task: Semi Decoder Architecture for Reverse Dictionary with SBERT Encoder

Sibaee,

Ahmad,

Khurfan

et al. 2023

Proceedings of ArabicNLP 2023

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A reverse dictionary takes a descriptive phrase of a particular concept and returns words with definitions that align with that phrase. While many reverse dictionaries cater to languages such as English and are readily available online or have been developed by researchers, there is a notable lack of similar resources for the Arabic language. This paper describes our participation in the Arabic Reverse Dictionary shared task. Our proposed method consists of two main steps: First, we convert word definitions into multidimensional vectors. Then, we train these encoded vectors using the SemiDecoder model for our target task. Our system secured 2nd place based on the Rank metric for both embeddings (Electra and Sgns).

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“…Recent REVDICT approaches utilize deep learning (DL) to map arbitrary-length definition phrases to the vector representation of the target word (Hill et al, 2016;Malekzadeh et al, 2021;Qi et al, 2020;Yan et al, 2020). The success of DL approaches indicates that REVDICT can be solved implicitly, i.e.…”

Section: Related Workmentioning

confidence: 99%

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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PREDICT: Persian Reverse Dictionary

Cited by 5 publications

References 0 publications

Abed at KSAA-RD Shared Task: Enhancing Arabic Word Embedding with Modified BERT Multilingual

Abed at KSAA-RD Shared Task: Enhancing Arabic Word Embedding with Modified BERT Multilingual

Qamosy at Arabic Reverse Dictionary shared task: Semi Decoder Architecture for Reverse Dictionary with SBERT Encoder

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

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