Word Embeddings in Low Resource Gujarati Language

Joshi, Ishani; Koringa, Purvi A.; Mitra, Suman K.

doi:10.1109/icdarw.2019.40090

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…The Word embeddings are distributed representations of text in an n-dimensional space. Every word in the vocabularies, [12] is associated with ndimensional vector of real numbers. The main goal is to have the vectors reduce the dimensionality of text while still capturing and maintaining meaning and relationships between words.…”

Section: Introductionmentioning

confidence: 99%

Neural Machine Translation for Low Resource Bantu Languages in East and Southern Africa

Omwoma,

Nderu,

Ogada

et al. 2024

Preprint

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Neural machine translation systems have recorded tremendous performance in data-intensive application, where the performance declines when we have limited training examples. In this paper we benchmark NMT between English and four African Bantu LRL pairs (Luganda, Swahili, Shona Tsonga [LSST]). Our aim was to evaluate current state of NMT for LRLs especially Bantu languages. Being the most morphological rich languages, but with OOV (Out of Vocabulary Problem) we proposed an NMT model using Multi-head self-attention. The model worked along with pre-trained BPE and Multi-BPE embeddings to develop a state-of the art translation system for low resourced morphological rich Bantu languages which have scarce translations online. We were able to address the issues with the publicly available corpora after refining the data for further use. We subjected our results to BLEU score for our system performance evaluation. Our experiments showed exemplary and first ever LRL translation BLEU score of; Eng.-Tsonga, Eng.-Swahili, Eng.-Shona and Eng.-Luganda as 62, 37, 22 and 20 respectively.

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Section: Introductionmentioning

confidence: 99%

Neural Machine Translation for Low Resource Bantu Languages in East and Southern Africa

Omwoma,

Nderu,

Ogada

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…To support scenarios where using huge amounts of data and computational resources is not feasible, it is important to continue developing our understanding of context-independent word embeddings, such as word2vec (Mikolov et al, 2013) and GloVe (Pennington et al, 2014). These algorithms continue to be used in a wide variety of situations, including the computational humanities (Abdulrahim, 2019;Hellrich et al, 2019) and languages where only small corpora are available (Joshi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Surprising Variability in Word Embedding Stability Across Languages

Burdick¹,

Kummerfeld²,

Mihalcea³

2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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Word embeddings are powerful representations that form the foundation of many natural language processing architectures, both in English and in other languages. To gain further insight into word embeddings, we explore their stability (e.g., overlap between the nearest neighbors of a word in different embedding spaces) in diverse languages. We discuss linguistic properties that are related to stability, drawing out insights about correlations with affixing, language gender systems, and other features. This has implications for embedding use, particularly in research that uses them to study language trends.

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Learning Syllables Using Conv-LSTM Model for Swahili Word Representation and Part-of-speech Tagging

Shivachi

Mokhosi

Zhou

et al. 2021

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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The need to capture intra-word information in natural language processing (NLP) tasks has inspired research in learning various word representations at word, character, or morpheme levels, but little attention has been given to syllables from a syllabic alphabet. Motivated by the success of compositional models in morphological languages, we present a Convolutional-long short term memory (Conv-LSTM) model for constructing Swahili word representation vectors from syllables. The unified architecture addresses the word agglutination and polysemous nature of Swahili by extracting high-level syllable features using a convolutional neural network (CNN) and then composes quality word embeddings with a long short term memory (LSTM). The word embeddings are then validated using a syllable-aware language model ( 31.267 ) and a part-of-speech (POS) tagging task ( 98.78 ), both yielding very competitive results to the state-of-art models in their respective domains. We further validate the language model using Xhosa and Shona, which are syllabic-based languages. The novelty of the study is in its capability to construct quality word embeddings from syllables using a hybrid model that does not use max-over-pool common in CNN and then the exploitation of these embeddings in POS tagging. Therefore, the study plays a crucial role in the processing of agglutinative and syllabic-based languages by contributing quality word embeddings from syllable embeddings, a robust Conv–LSTM model that learns syllables for not only language modeling and POS tagging, but also for other downstream NLP tasks.

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Word Embeddings in Low Resource Gujarati Language

Cited by 3 publications

References 3 publications

Neural Machine Translation for Low Resource Bantu Languages in East and Southern Africa

Neural Machine Translation for Low Resource Bantu Languages in East and Southern Africa

Analyzing the Surprising Variability in Word Embedding Stability Across Languages

Learning Syllables Using Conv-LSTM Model for Swahili Word Representation and Part-of-speech Tagging

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