INTERNATIONAL JOURNAL OF RESEARCH AND INNO V A T ION IN APPLIED SCIENCE (IJRIAS)

ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |V o lume X Issue X October 2025

Enhancing an RNN-Attention Yoruba Text Autocompletion System

through an Optimized Adam Framework

Oluokun Samuel Olugbenga^1*, Ayeni Joshua Ayobami², Adebunmi Adefunso³, Oladayo Ezekiel

Makinde⁴, Adebayo Ademola Riliwan⁵

¹Department of Information Systems and Technology, Kings University, Odeomu, Nigeria

²Department of Computer Science, Ajayi Crowther University, Oyo, Nigeria

³Athenahealth, Boston Massachusetts, USA

⁴Department of Computer Science, Ajayi Crowther University, Oyo, Nigeria

⁵Federal School of Surveying, Oyo, Nigeria

DOI: https://doi.org/10.51584/IJRIAS.2025.10100000170

Received: 06 November 2025; Accepted: 13 November 2025; Published: 20 November 2025

ABSTRACT

The development of effective neural models for low-resource languages is fundamentally constrained by two

interrelated factors: architectural suitability for linguistic complexity and optimization stability on small

datasets. This research addresses the critical yet under-explored challenge of optimization instability for

character-level sequence modeling in Yoruba, a morphologically rich and tonal language. We posit that

standard adaptive optimizers like Adam, while performant in high-resource contexts, introduce convergence

pathologies in low resource settings due to volatile gradient estimates and an inability to adapt to sparse loss

landscapes. To address this, we propose a principled enhancement to the Adam optimizer, integrating a

dynamic learning rate scheduler, gradient norm clipping, and a strategically determined batch size. This

Enhanced Adam framework is applied to a character-level Recurrent Neural Network augmented with a multi-

head attention mechanism, an architecture designed to handle Yoruba's agglutinative and tonal features. In a

rigorous comparative study, the model trained with our Enhanced Adam optimizer achieved a perplexity of

2.07, a statistically significant 8.5% improvement over the identical architecture trained with standard Adam

(perplexity 2.26). More importantly, the enhanced framework demonstrably improved training stability,

accelerated convergence, and yielded a better-calibrated model. This work establishes that targeted optimizer

engineering is not merely an implementation detail but a critical research direction for unlocking the full

potential of advanced neural architectures in low-resource Natural Language Processing (NLP), providing a

reproducible and transferable methodology for other underserved languages.

Keywords: Low-Resource NLP, Yoruba Language, Text Autocompletion, Adam Optimizer, Optimization

Stability, Gradient Clipping, Learning Rate Scheduling, RNN, Attention Mechanism.

INTRODUCTION

The transformative advances in Natural Language Processing (NLP) over the past decade, driven by deep

learning, have predominantly served a handful of high-resource languages such as English, Mandarin, and

Spanish (Joshi et al., 2020). This has created a significant digital divide, leaving speakers of thousands of other

languages without access to foundational technologies like accurate machine translation, robust speech

recognition, and intelligent writing assistants (Blasi et al., 2022). Among these underserved languages is

Yoruba, a major Niger-Congo language spoken by over 40 million people in West Africa and the diaspora. The

lack of NLP tools for Yoruba impedes digital inclusion, hinders educational and economic opportunities, and

contributes to the erosion of linguistic diversity in the digital sphere (Adelani et al., 2021).

Developing NLP tools for a language like Yoruba presents a dual challenge. The first challenge is architectural:

designing models that can effectively capture the language's unique linguistic characteristics. Yoruba is tonal,

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where meaning is lexically determined by pitch patterns on vowels (e.g., igbá calabash vs. igbà time), and

agglutinative, forming complex words through the linear combination of morphemes (Adeniyi, 2020; Akinola

et al., 2021). These properties make sub-word or character-level modeling more appropriate than word-level

modeling, as the latter fails to capture the nuanced, compositional structure of words (Bostrom & Durrett,

2020).

The second, often understated challenge, is optimization. State-of-the-art neural architectures, often comprising

millions of parameters, are data-hungry. In low-resource settings, the scarcity of data leads to a poorly defined

optimization landscape. Standard optimization algorithms, most notably the Adam optimizer (Kingma & Ba,

2015), which is ubiquitous in modern deep learning, were designed and tuned for large-scale datasets. When

applied to small corpora, Adam and its variants can exhibit pathological behaviors: unstable convergence due

to noisy gradient estimates, vulnerability to exploding gradients in deep recurrent networks, and an inability to

escape sharp local minima due to a fixed or poorly adapted learning rate schedule (Zhang et al., 2020; Wilson

et al., 2017). Consequently, a powerful architecture may never reach its potential because the training process

is inherently unstable and inefficient.

While previous work, including our own, has successfully demonstrated the efficacy of attention-augmented

RNNs for Yoruba text autocompletion (Oluokun et al., 2025), the optimization process itself was treated as a

static component. This paper directly addresses this gap by making optimization the central subject of inquiry.

We hypothesize that a purpose-built optimization framework is essential to stabilize training and maximize the

performance of complex models on low-resource data.

The primary contributions of this research are:

1. A detailed analysis of the optimization challenges specific to training character-level RNN-Attention

models on a small Yoruba corpus.

2. The design and implementation of an Enhanced Adam optimization framework that systematically

incorporates dynamic learning rate scheduling, gradient clipping, and strategic batch processing to

mitigate these challenges.

3. An empirical evaluation demonstrating that the proposed framework yields a statistically significant

improvement in model performance (perplexity) and training stability compared to the standard Adam

optimizer, using an identical neural architecture.

4. The provision of a robust, reproducible methodology that can be adapted for developing NLP systems

for other low-resource languages.

This work argues that for the field of low-resource NLP to mature, optimizer engineering must be recognized

as a discipline as critical as architectural innovation.

LITERATURE REVIEW

2.1 NLP for Low-Resource and Morphologically Rich Languages

The plight of low-resource languages in NLP has garnered increasing attention. Initiatives like MasakhaNEWS

(Adelani et al., 2023) for news classification and MasakhaNER (Adelani et al., 2021) for named entity

recognition have created crucial benchmarks for African languages. For Yoruba specifically, research has

focused on diacritic restoration (Ogheneruemu et al., 2023; Akindele et al., 2024), part-of-speech tagging

(Ugwu et al., 2024), and machine translation (Dossou & Emezue, 2021). A common thread is the need for

models that operate at the sub-word level. Bostrom & Durrett (2020) showed that character-level models often

outperform word-level models on morphologically complex tasks because they can handle out-of-vocabulary

words and model morphological processes directly. This justifies our choice of a character-level modeling

approach for Yoruba autocompletion.

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2.2 Neural Architectures for Sequence Prediction

Recurrent Neural Networks (RNNs), and specifically Long Short-Term Memory (LSTM) networks (Hochreiter

& Schmidhuber, 1997), have been the historical standard for sequence prediction tasks like text generation and

autocompletion (Sutskever et al., 2011). However, their sequential nature and tendency to "forget" information

from the distant past due to vanishing gradients limit their effectiveness. The attention mechanism (Bahdanau

et al., 2015; Vaswani et al., 2017) revolutionized sequence modeling by allowing the model to directly

reference any part of the input sequence, effectively creating a dynamic, content-addressable memory. While

Transformers have largely superseded RNNs in high-resource settings, their data efficiency is poor. Therefore,

an RNN augmented with attention presents a compelling middle ground: it retains the inductive bias for

sequential processing while gaining the representational power of attention, making it potentially more suitable

for lowresource scenarios (Al-Anzi & Shalini, 2024; Vanama et al., 2023).

2.3 Optimization in Deep Learning

The Adam optimizer (Kingma & Ba, 2015) is an adaptive learning rate algorithm that combines the advantages

of AdaGrad (Duchi et al., 2011) and RMSProp (Tieleman & Hinton, 2012). It maintains per-parameter learning

rates based on estimates of the first and second moments of the gradients. While its adaptive nature leads to

rapid initial convergence, it has known limitations. Wilson et al. (2017) argued that adaptive methods often

generalize worse than stochastic gradient descent (SGD) with momentum because they can converge to sharp

minima. This issue is exacerbated with small datasets where the gradient noise is high.

While Adam's adaptive nature facilitates rapid initial convergence, its performance, particularly in low-

resource scenarios, is highly sensitive to its configuration and can be prone to instability and poor

generalization (Wilson et al., 2017). To mitigate these issues, two key families of techniques are essential for

stabilizing Adam in practice:

Gradient Clipping: This technique is critical for preventing the exploding gradient problem, which is

especially prevalent in recurrent architectures (Pascanu et al., 2013). By constraining the L2-norm of the

gradient vector to a predefined threshold (e.g., clip_value = 1.0), it ensures that parameter updates remain

within a stable range, preventing destructive large steps that can derail the optimization process. The operation

is defined as as g 2=min ( g 2, clip_value)

g

2=min (

g

2, clip_value).

Learning Rate Scheduling and Regularization: A fixed learning rate can hinder convergence to a precise

minimum. Dynamic scheduling, such as the Reduce LR On Plateau scheduler which reduces the learning rate

by a factor (e.g., 0.5) upon a loss plateau, refines the optimization trajectory in later stages. Furthermore,

integrating weight decay as an L2 regularization term directly into the update rule (λθtλθt) is a principled

method to prevent overfitting and encourage simpler models by penalizing large weights, leading to better

generalization.

However, most research on optimizer behavior is conducted in high-resource contexts. The specific failure

modes of Adam and the precise calibration of stabilization techniques for low-resource, character-level NLP

remain an open area for investigation, which this paper directly addresses.

2.4 Related work

Akindele et al., (2024) study aimed to address the lack of a standard benchmark for evaluating Yoruba

diacritization systems and improve automatic diacritization using lightweight models. Yoruba, a tonal

language, relies heavily on diacritics for meaning and pronunciation, making accurate diacritization essential.

Manual diacritization is time-consuming, necessitating automated solutions. The researchers introduced the

Yoruba Automatic Diacritization (YAD) dataset, derived from MENYO-20k, and pre-trained T5 models (Oyo-

T5) specifically for Yoruba. They compared these models with multilingual T5 variants (MT5, AfriMT5,

AfriTeVaV2, UMT5) to evaluate performance. The methodology involved pre-training Oyo-T5 models of

varying sizes

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(tiny to base) and fine-tuning them on YAD, Bible, and JW300 datasets. The models were evaluated using

SacreBLEU and ChrF metrics on YAD, Global Voices (GV), and Bible test sets. Results showed that Oyo-

T5base outperformed larger multilingual models, and increasing model size and training data improved

performance. Notably, Oyo-T5-small (60M parameters) surpassed AfriTeVa-base (313M parameters). The

study concluded that more data and larger models enhance Yoruba diacritization, with Oyo-T5-base achieving

the best results. The YAD dataset and models were released on GitHub for reproducibility.

Ahia et al., (2024) study addressed the limitations in Yoruba natural language processing (NLP) by developing

resources and models for regional Yoruba dialects. Despite Yoruba having over 47 million speakers, existing

natural language processing research focused primarily on the standard dialect, neglecting regional variations.

This gap led to disparities in automatic speech recognition (ASR), machine translation (MT), and speech-to-

text translation (S2TT) performance for non-standard dialects. The research sought to create a high-quality

parallel corpus, YORÙLECT, covering four Yoruba dialects, Standard Yoruba, Ifè, Ìlàje, and Ìjèbù across

religious, news, and TED Talk domains. It aimed to evaluate the zero-shot performance of state-of-the-art

natural language processing models on these dialects and fine-tune them to improve performance. Native

speakers were engaged to curate a dataset comprising 1,506 parallel text sentences per dialect and 9 hours of

recorded speech. Preprocessing involved text normalization, phonetic transcription, and segmentation. Speech

data were recorded in sound-isolated environments. Standard natural language processing models like NLLB-

600M (for MT), MMS and Whisper (for ASR), and SeamlessM4T (for S2TT) were tested, and dialect-specific

fine-tuning was applied. Performance evaluation revealed that standard Yoruba outperformed regional dialects

across all tasks, highlighting a lack of robustness in existing models. Zero-shot MT results showed Google

Translate performed best but had a significant performance gap between standard and regional Yoruba. After

fine-tuning, BLEU scores improved by 14 points, and ASR word error rates decreased by 20 points. S2TT

remained the most challenging, with limited improvement post-finetuning.

METHODOLOGY

3.1 Experimental Design and Research Questions

This research employs a controlled, comparative experimental design. The independent variable is the

optimization strategy, and the dependent variables are the model's final performance (perplexity, accuracy) and

training dynamics (loss convergence, stability). The neural architecture a character-level RNN with a multi-

head attention mechanism is held constant across experiments to isolate the effect of the optimizer.

The research seeks to answer the following questions:

1. RQ1: What are the characteristic signs of optimization instability when training an RNN-Attention

model on a low-resource Yoruba dataset with the standard Adam optimizer?

2. RQ2: To what extent does the proposed Enhanced Adam framework mitigate these instability issues, as

measured by training loss curves and variance?

3. RQ3: Does the Enhanced Adam framework lead to a statistically significant improvement in the final

model's predictive performance and confidence on a held-out test set?

3.2. Data Collection and Preprocessing

Due to the lack of a large-scale digital corpus for Yoruba, a custom dataset was curated for this study. Both

models were trained on the same dataset extracted from "fdata.xlsx", containing 4,431 words with their

accented

and

non-accented

versions.

The

dataset

was

downloaded

https://www.kaggle.com/datasets/adeyemiquadri1/ne w-yoruba-data . The dataset was split into training (80%),

validation (10%), and test (10%) sets. The vocabulary consists of 22 unique characters, and the maximum

sequence length is 11 characters.

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Figure 3.1: Research Dataset

3.3 Data Preprocessing

The preprocessing pipeline (Figure 3.1) was designed to preserve Yorùbá's phonological and orthographic

integrity while converting text into a numerical representation. It consisted of the following steps:"

3.3.1. Text Normalization and Cleaning

The raw text corpus underwent a rigorous normalization process to ensure consistency and eliminate noise.

This involved:

Diacritic Preservation: All Yorùbá-specific diacritical marks (e.g., ẹ, ọ, ṣ, à, è, ì, ò, ù) were meticulously

preserved, as they are phonemically critical and determine lexical meaning.

Noise Removal: Non-linguistic artifacts, including numerical digits, punctuation marks (except for relevant

sentence delimiters used in sequence creation), and extraneous whitespace characters were systematically

removed.

Case Normalization: All text was converted to lowercase to maintain a consistent vocabulary and reduce

sparsity, a standard practice in character-level modeling.

3.3.2 Character-Level Tokenization

Given Yorùbá's agglutinative morphology, where words are formed by combining morphemes, character-level

tokenization was explicitly chosen over word-level tokenization. This approach allows the model to learn

subword morphological units and generate novel, valid words not present in the training data. The tokenization

process segmented the normalized text into its constituent characters, treating each character, including spaces

and diacritical marks, as a discrete token. For example, the phrase "Ẹ kú àárọ" was decomposed into the

̀

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sequence: ['Ẹ', ' ', 'k', 'ú', ' ', 'à', 'á', 'r', 'ọ', '̀']. A vocabulary of 22 unique characters was constructed from the

entire corpus, with each character mapped to a unique integer index.

Figure 3.2: Character-Level Tokenization

3.3.3 Sequence Creation and Sliding Window

The stream of character tokens was structured into input-output pairs to formulate a supervised learning

problem. A sliding window of a fixed sequence length (n = 10) was passed over the tokenized text. For each

position of the window, the first n characters formed the input sequence (X), and the immediate next character

was the target label (y). This generated a large number of training examples from the limited corpus.

Example:

Given a sequence length of 5 and the text "ẹkọ", the following training samples were created:

Input: ['Ẹ', ' ', 'k', 'ú', ' '] → Target: 'à'

Input: [' ', 'k', 'ú', ' ', 'à'] → Target: 'á'

Input: ['k', 'ú', ' ', 'à', 'á'] → Target: 'r'

Figure 3.3: Sequence Creation and Sliding Window

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3.4. Model Training Environment and Configuration

3.4.1. Experimental Setup

All models were developed and trained on a dedicated research workstation with the following specification:

GPU: NVIDIA GeForce RTX 3080 (10GB GDDR6X VRAM)

CPU: Intel Core i7-11700K @ 3.60GHz

RAM: 32GB DDR4

Operating System: Ubuntu 20.04.4 LTS

This hardware configuration was selected to facilitate the rapid iteration of experiments necessary for the

model design.

3.4.2. Implementation Framework

The models were implemented using Python 3.8.10. The deep learning framework of choice was TensorFlow

(v2.6.0) with its high-level API, Keras, which provides the necessary flexibility for custom layer

implementation (the multi-head attention mechanism) alongside robust training utilities. Key Python libraries

utilized for data manipulation and numerical computation included NumPy (v1.21.2) and Pandas (v1.3.3).

Table 3.1: Yoruba Dataset Statistics after Preprocessing

Statistic

Total Words in Corpus

Value

4,431

Unique Characters (Vocabulary Size) 22

Training Sequences

Validation Sequences

Test Sequences

7,740

968

11

Maximum Sequence Length

3.5 Model Architecture

3.5.1 RNN with Attention Mechanism

The architecture of the proposed RNN + Attention model is depicted in Figure 3.4 and its parameters are

detailed in Table 3.2.

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Figure 3.4: RNN + Attention Architecture

Table 3.2: RNN+Attention Model Parameters

Parameter

Embedding dimension

Value

64

Hidden dimension (LSTM units) 128

Number of LSTM layers

Number of attention heads

Dropout rate

Vocabulary size

Learning rate

2

4

0.5

22

0.001

64

10

Batch size

Number of epochs

The model consists of an embedding layer, two LSTM layers, a multi-head attention mechanism, and layer

normalization.

3.5.3 Enhanced RNN + Adam Optimizer

The Optimized RNN model builds upon the RNN architecture by incorporating Adam Optimizer. This allow

the model to find the optimal set of parameters for the LSTM that minimizes prediction errors. During training,

the model will make wrong guesses this allow the model progressively better at predicting the next word or

character in a sequence.

Figure 3.5: Enhanced RNN + Adam Optimizer

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Table 3.3: RNN + Attention and Adam Optimizer Model Parameters

Parameter

Embedding dimension

Value

128

Hidden dimension (LSTM units) 256

Number of LSTM layers

Number of attention heads

Dropout rate

Vocabulary size

Optimizer

2

4

0.5

22

Adam

0.001

64

Learning rate

Batch size

Number of epochs

10

3.5.4 Enhanced RNN + Enhanced Adam Optimizer Architecture

The RNN + Attention + Enhanced Adam Optimizer architecture depicted here represents the culmination of the

research's methodological innovation, delivering the optimal performance that forms the core of its

contribution. This model is a significant evolution beyond its predecessors, incorporating a more complex,

deeper hierarchy that includes a Bidirectional LSTM to capture both past and future contexts, followed by two

separate MultiHead Attention layers (with 6 and 4 heads) interspersed with residual connections and Layer

Normalization. This design explicitly addresses the challenges of gradient flow and feature reuse in deep

networks, enabling a more nuanced understanding of Yorùbá's linguistic structure.

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Figure 3.6: Enhanced RNN + Enhanced Adam Optimizer

Table 3.5: RNN + Attention and Enhanced Adam Optimizer

Parameter

Embedding dimension

Value

128

Hidden dimension (LSTM units) 256

Number of LSTM layers

Number of attention heads

Dropout rate

2

6

0.5

Vocabulary size

Optimizer

22

Enhanced Adam

Learning rate

Batch size

0.001

64

Number of epochs

10

3.6 Optimization Frameworks: Standard vs. Enhanced Adam

This research employs a controlled comparative analysis where the independent variable is the optimization

strategy applied to an identical RNN-Attention architecture. Two distinct frameworks were implemented.

3.6.1 The Standard Adam Baseline (Model M1)

This configuration represents a common, out-of-the-box application of Adam, serving as the experimental

baseline.

1. Optimizer: Adam with default parameters: learning rate α=0.001α=0.001, β1=0.9β1=0.9, β2=0.999β2

=0.999, ϵ=10−8ϵ=10−8.

2. Batch Size: 64.

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3. Loss Function: Categorical Cross-Entropy.

4. Epochs: 10, with early stopping (patience of 5 epochs on validation loss).

3.6.2 The Enhanced Adam Framework (Model M2)

Our proposed framework augments the standard Adam with a suite of stabilization techniques informed by the

challenges of low-resource optimization.

Optimizer: Adam with integrated weight decay (λ=0.01λ=0.01), modifying the effective update to include a

direct penalty on large weights.

Dynamic Learning Rate Scheduling: A ReduceLROnPlateau scheduler was employed, monitoring validation

loss and reducing the learning rate by a factor of 0.5 after 5 epochs of no improvement, with a lower bound of

10−610−6.

Gradient Clipping: Gradients were clipped to a maximum global L2-norm of 1.0 during backpropagation to

prevent explosion.

Strategic Batch Processing: A batch size of 64 was maintained, providing a balance between stable gradient

estimation and computational efficiency on the small dataset.

Loss Function & Epochs: Identical to M1 to ensure a fair comparison.

3.7. Evaluation Metrics

The model was evaluated using the following metrics on the held-out test set:

•

Perplexity: Measures the model's prediction uncertainty. Lower perplexity indicates better

performance.

Perplexity =

(

)

(9)

=1

2

•

Top-K Accuracy: The percentage of test cases where the true next character is among the top K model

predictions (K=1, 3, 5).

•

Mean Reciprocal Rank (MRR): Measures the average rank of the first correct suggestion.

MRR = (1/N) Σ (1/r_i) (10)

BLEU Score: Assesses the fluency and quality of the generated character sequences by comparing them to a

reference.

EXPERIMENTAL RESULTS AND ANALYSIS

4.1 Training Dynamics and Stability (Addressing RQ1 & RQ2)

The training process for both models was meticulously logged. Figure 2 illustrates the training and validation

loss curves for M1 (Standard Adam) and M2 (Enhanced Adam) over the course of 10 epochs.

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Figure 4.1: Comparative Training Loss Curves

The training trajectories for M1 and M2 revealed significant differences in stability. M1 exhibited

characteristic instability with significant oscillations in both training and validation loss. In contrast, M2

demonstrated markedly superior stability, with gradient clipping eliminating large loss spikes and the dynamic

learning rate scheduler facilitating a smooth descent. The variance in M2's validation loss was substantially

reduced, signifying a more robust optimization process and providing a clear affirmative answer to RQ1 and

RQ2.

4.1.2 Discussion of Experimental Results: A Comparative Analysis of Optimization Frameworks

The empirical evaluation of the baseline versus optimized Adam configurations reveals profound differences in

training behavior, convergence properties, and model robustness. The following analysis dissects the results

presented in Tables 3.1, 3.2, and 3.3 to provide a rigorous interpretation of the optimizer's impact.

4.1 Analysis of Performance Metrics (Table 4.1)

Table 4.1: Performance Metrics

Metric

Loss Variance

Baseline Model

0.0223

Optimized Model

0.0098

Standard Deviation 0.1494

0.0990

The critical evidence for the optimized model's superiority lies in the Loss Variance and Standard Deviation

metrics. The optimized configuration demonstrates a 56.1% reduction in variance (from 0.0223 to 0.0098) and

a 33.7% reduction in standard deviation (from 0.1494 to 0.0990). This indicates a dramatically more stable and

predictable training process. The lower variance signifies that the optimizer is making consistent, reliable

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progress, whereas the high variance of the baseline suggests a volatile and unreliable optimization trajectory,

highly sensitive to mini-batch noise.

4.1.2 Analysis of Convergence Dynamics (Table 4.2)

Table 4.2: Convergence Analysis

Convergence Metric

Epochs to Converge

Baseline Model

60

Optimized Model

6

Average Improvement per Epoch -0.0023

Stability Window 75

-0.0070

85

Table 4.2 provides unequivocal evidence of the enhanced efficiency of the optimized Adam framework. The

most striking result is the order-of-magnitude reduction in Epochs to Converge, from 60 for the baseline to a

mere 6 for the optimized model. This 90% reduction in convergence time is a direct consequence of the

synergistic enhancements. The combination of a well-initialized learning rate (α=0.001), gradient clipping

preventing destabilizing updates, and the dynamic ReduceLROnPlateau scheduler allows the model to navigate

the loss landscape far more efficiently.

This accelerated convergence is further supported by the Average Improvement per Epoch, which is more than

three times greater for the optimized model (-0.0070 vs. -0.0023). Each epoch of training for the optimized

configuration yields substantially more progress, indicating that the optimizer is not only faster but also more

effective per computational unit. Furthermore, the increased Stability Window (85 vs. 75 epochs) suggests that

once converged, the optimized model maintains its performance for a longer duration, exhibiting greater

resilience to potential late-training divergence or oscillations.

4.1.3 Analysis of Training Stability (Table 4.3)

Table 4.3: Stability Analysis

Stability Metric

Loss Oscillation

Sub-3.0 Loss Epochs 100

Baseline Model

0.0181

Optimized Model

0.0131

99

The stability metrics in Table 4.3 corroborate the findings from the previous tables, highlighting the qualitative

improvements in the training process. The Loss Oscillation metric, which quantifies the volatility of the

training curve, is 27.6% lower in the optimized model (0.0131 vs. 0.0181). This smoothing effect is a direct

outcome of two key techniques: gradient norm clipping and mini-batch processing. By constraining the

gradient norm to a maximum of 1.0, the optimizer prevents pathological parameter updates that cause large

loss spikes. Simultaneously, using a batch size of 32 provides a more accurate, lower-variance estimate of the

true gradient direction than a stochastic estimate, leading to a smoother descent path.

The near-identical count of Sub-3.0 Loss Epochs (100 vs. 99) indicates that both models are capable of

reaching a reasonable performance threshold. However, this metric alone is insufficient. When interpreted in

the context of the convergence analysis, it reveals a more nuanced story: the optimized model achieves a stable

and high performing state almost immediately (within 6 epochs) and maintains it, whereas the baseline

requires 60 epochs of volatile training to reach a similar plateau. The stability of the optimized model's

performance is far superior, making it more reliable and computationally efficient for practical deployment.

4.1.4 Synthesis and Interpretation

In summary, the results presented across all three tables paint a coherent picture of the optimized Adam

framework's superiority. The metrics of success are the dramatic acceleration of convergence (Table 4.2) and

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the significant enhancement of training stability (Table 4.3). These improvements are attributable to the

principled integration of weight decay, adaptive learning rate scheduling, gradient clipping, and strategic batch

processing. This configuration transforms the standard Adam optimizer from a volatile, data-inefficient

algorithm in low-resource settings into a robust, stable, and highly efficient engine for model training. This

work underscores that optimizer calibration is not a minor implementation detail but a critical research axis for

achieving state-of-the-art performance in challenging domains like low-resource language modeling.

4.2 Performance Evaluation

The performance of both models is summarized in Table 4.4. The primary result is the improvement in

perplexity. The reduction from 2.26 to 2.07 represents an 8.5% improvement. While both models achieve

perfect Top-K accuracy on this test set, the lower perplexity indicates that the Enhanced Adam model is more

confident and better-calibrated, providing a strong affirmative answer to RQ3.

Table 4.4: System Performance Evaluation

Metric

RNN + Attention Mechanism and Adam RNN + Attention Mechanism and

Enhanced Adam

2.26

1.0

2.07

1.0

Perplexity

Top-1 Accuracy

Metric

RNN + Attention Mechanism and Adam RNN + Attention Mechanism and

Enhanced Adam

1.0

Top-3 Accuracy

Top-5 Accuracy

Mean Reciprocal Rank (MRR)

BLEU Score

Figure 4.2: Performance Comparison of RNN and RNN + Attention

Performance Comparison

5

0

Perplexity

Top-1-Acc

Top-5-Acc

MRR

BLUE

RNN + Attention Mechanism

RNN + Attention Mechanism and Enhanced Adam

4.3. Training Dynamics: Loss and Accuracy

The comparative analysis of training dynamics reveals the profound efficacy of the Enhanced Adam

framework, as the optimized model exhibits a rapid, monotonic descent to a lower loss plateau with minimal

oscillation, starkly contrasting the volatile, high-variance trajectory of the standard Adam baseline. This

accelerated and stabilized convergence, facilitated by gradient clipping and adaptive learning rate scheduling,

is further substantiated by the model's internal mechanics, where visualized attention weights demonstrate

sharp, linguistically coherent alignments attending to critical tonal and morphological features in Yoruba text.

This synergy of enhanced optimization and superior representational quality underscores that targeted

optimizer engineering is indispensable for unlocking the full potential of complex neural architectures in low-

resource, linguistically rich environments.

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Figure 4.3: Training Loss Comparison across RNN Architectures

Figure 4.4: Training Accuracy Comparison across RNN Architectures

4.4. Ablation Study

To quantitatively deconstruct the individual and synergistic contributions of the components comprising our

Enhanced Adam framework, a rigorous ablation study was conducted. This analysis is critical for

understanding whether the observed performance gains stem from a synergistic interplay of all components or

are driven by a single dominant technique. The identical RNN-Attention architecture, as detailed in Section

3.3, was trained under five distinct optimization configurations, with results evaluated on the held-out test set.

The configurations and their corresponding results are summarized in Table 5.1.

Table 4.5: Ablation Study Results on Test Set Performance

Configuration Optimizer Components

Perplexity (↓)

A (Baseline)

Standard Adam

2.21

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B

C

D

Adam + Weight Decay (λ=0.01)

Adam + Grad. Clipping (norm=1.0) 2.15

Adam + LR Scheduling

2.17

2.13

2.07

E (Full Model) Enhanced Adam (All)

Each component provided a standalone benefit, but their combination was synergistic:

• Weight Decay (B) acted as an effective regularizer.

• Gradient Clipping (C) had the most pronounced effect on training stability.

• Learning Rate Scheduling (D) yielded the most significant improvement in convergence speed.

• The Full Enhanced Adam framework (E) outperformed every ablated configuration, achieving the lowest

perplexity and fastest convergence, validating the holistic approach.

4.5.5 Discussion of Ablation Findings

The ablation study conclusively demonstrates that each component of the Enhanced Adam framework

addresses a distinct facet of the optimization pathology in low-resource settings. While each technique

provides a standalone benefit, their combination is non-linear and mutually reinforcing. Gradient clipping

stabilizes the step size, learning rate scheduling optimizes the step direction over time, and weight decay

shapes the solution space towards generalizability. The results validate that optimizer engineering for low-

resource NLP requires a holistic, multi-faceted approach rather than relying on a single silver-bullet technique.

This principled methodology provides a reproducible blueprint for stabilizing complex neural architectures on

data-scarce tasks, with significant implications for the development of NLP tools for other underserved

languages.

4.6. Qualitative Error Analysis

To complement the quantitative metrics and provide a deeper understanding of the models' performance, a

qualitative analysis of the text completions was conducted. We manually examined the top-5 suggestions

generated by both the baseline model (M1: Standard Adam) and the proposed model (M2: Enhanced Adam)

for various input sequences from the test set.

Table 4.6: Qualitative Examples of Model Predictions

Input

Target

M1 (Standard M2 (Enhanced Observations

Sequence Character Adam) - Top 3 Adam) - Top 3

(Context)

Predictions

(Confidence) (Confidence)

"ẹ kú " "à"

1.

(0.41)

2.

(0.22)

3.

"à"

"i"

1.

(0.58)

2.

(0.15)

3.

"à"

"i"

Both models correctly predict the target "à" to form the

common greeting "ẹ kú àárọ". However, M2

̀

demonstrates significantly higher confidence in the

correct prediction (0.58 vs. 0.41), indicating a

bettercalibrated probability distribution.

"ọ"

(0.18)

(0.12)

"ilé iṣ" "é"

1. "ẹ"

(0.38) 2.

"é" (0.35)

3. "e" (0.10)

1. "é"

(0.49) 2.

"ẹ" (0.28)

3. "e" (0.09)

This is a critical example. The sequence "ilé iṣ" likely

leads to the word "ilé iṣé" (place of work). M2 correctly

identifies the tonal character "é" as the most likely, while

M1 incorrectly favors the nontonal "ẹ". This shows M2's

superior ability to model Yoruba's tonal nuances.

Input

Target

M1 (Standard M2 (Enhanced Observations

Sequence Character Adam) - Top 3 Adam) - Top 3

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(Context)

Predictions

(Confidence) (Confidence)

"ọjọ a" "t"

1.

(0.30)

2.

(0.29)

3.

(0.11)

1.

(0.33)

2.

(0.25)

3.

"t"

1.

(0.45)

2.

(0.21)

3.

(0.08)

1.

(0.52)

2.

(0.18)

3.

"t"

For this input, leading to "ọjọ ati" (day and), both

models rank the correct character "t" first. The key

difference again lies in the confidence level, with M2

being more decisive (0.45 vs. 0.30). The reduced

confidence for the distractor "r" in M2 also indicates a

sharper focus.

The sequence "awọn ọ" commonly precedes "ọmọ"

(child), making "m" the target. M2 achieves a much

higher confidence for the correct character and

significantly suppresses the probabilities of incorrect

alternatives ("k", "j"), leading to a cleaner and more

reliable suggestion list.

"r"

"k"

"m"

"k"

"j"

"r"

"k"

"m"

"k"

"j"

"awọn ọ" "m"

(0.19)

(0.10)

DISCUSSION OF QUALITATIVE FINDINGS

The qualitative analysis reveals nuanced performance differences that are not captured by the saturated Top-K

accuracy scores.

1. Superior Confidence Calibration: In all cases where both models predicted the correct character, the

model trained with the Enhanced Adam optimizer (M2) assigned a substantially higher probability to

the correct suggestion. This aligns perfectly with the lower overall perplexity reported in Table 4.4 and

translates to a more reliable user experience in a real-world autocompletion system, with less

"flickering" between top suggestions.

2. Improved Handling of Linguistic Complexity: Example 2 provides direct evidence that the Enhanced

Adam framework leads to a qualitatively better language model. The ability of M2 to correctly

prioritize the tonal character "é" over "ẹ" in the context of "ilé iṣé" demonstrates its enhanced capability

to learn and apply the morpho-phonological rules of Yoruba. The stabilized training dynamics of the

Enhanced Adam optimizer likely allow the RNN-Attention architecture to form more robust

representations of these critical linguistic features.

In conclusion, while both models technically achieve perfect Top-K accuracy on the test set, the model trained

with our Enhanced Adam framework produces a superior probability distribution. It is not only more confident

in its correct predictions but also shows a better grasp of the language's structural complexity, making it a

fundamentally higher-quality model for the task.

4.7 Statistical Significance Testing

To substantiate the claim of statistically significant improvement and ensure the observed gains are not due to

random variation, we performed a rigorous statistical analysis. Both the baseline model (M1) and the proposed

model (M2) were trained and evaluated over ten independent runs with different random seeds. A paired t-test

was conducted on the final perplexity scores from these runs.

The results confirm a statistically significant difference:

•

Mean Perplexity (M1): 2.265

Mean Perplexity (M2): 2.075

t-statistic: t = 5.42

•

p-value: p = 0.0003

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With a p < 0.05, we reject the null hypothesis, concluding that the performance improvement achieved by the

Enhanced Adam framework is statistically significant.

DISCUSSION

This research demonstrates that the choice and configuration of the optimizer are not secondary concerns but

are integral to the success of low-resource NLP projects. The standard Adam optimizer, while a powerful

algorithm, is not a one-size-fits-all solution. Its default parameters, particularly the fixed learning rate and lack

of gradient control, are suboptimal for the volatile optimization landscape of a small dataset. Our Enhanced

Adam framework acts as a necessary stabilization package, ensuring that the sophisticated RNN-Attention

architecture can be trained effectively.

There is a synergistic relationship between the model architecture and the optimizer. The RNN-Attention

model provides the capacity to learn complex Yoruba patterns, but the Enhanced Adam optimizer provides the

stability and guidance for that learning to occur efficiently. The attention mechanism, which allows the model

to focus on relevant context, is complemented by the optimizer's ability to navigate the loss landscape without

being derailed by noise or exploding gradients. This synergy is likely a key factor in achieving state-of-the-art

performance for this task.

The implications of this work extend beyond Yoruba text autocompletion. The methodology presented

identifying optimizer instability and systematically addressing it with calibrated techniques is a transferable

blueprint. Researchers working on other low-resource languages can adopt a similar approach: start with a

sensible architecture, diagnose training pathologies, and then engineer the optimization process to resolve

them. This shifts the paradigm from merely importing model architectures from high-resource NLP to actively

developing the supporting infrastructure, like robust optimizers, required for their success in data-scarce

environments.

6. Conclusion and Future Work

This research has established that optimizer engineering is a critical frontier in low-resource NLP. We

successfully developed an Enhanced Adam framework that, through the integration of dynamic learning rate

scheduling, gradient clipping, and weight decay, significantly stabilizes the training of an RNN-Attention

model for Yoruba text autocompletion. The result was a model with not only better quantitative performance

(an 8.5% reduction in perplexity) and superior training stability but also, as the qualitative analysis revealed,

better confidence calibration and a stronger grasp of Yoruba's tonal nuances.

While the model demonstrates exceptional performance on the current dataset, it is important to acknowledge

that the research is constrained by the scale of the corpus. Future work will prioritize the expansion of this

dataset by incorporating diverse textual sources, including contemporary web content, literature, and

transcribed oral narratives, to enhance the model's robustness, dialectal coverage, and vocabulary.

This work opens several promising avenues for future research, for which we propose the following roadmap:

1. Automated Hyperparameter Tuning: Employ large-scale Bayesian optimization or a similar strategy

to find the optimal values for the clipping threshold, scheduler patience, and decay factor, rather than

relying on empirically chosen values, to further maximize performance.

2. Generalization to Transformer-Based Architectures: A key next step is to evaluate the

generalizability of the proposed Enhanced Adam framework by applying it to pure Transformer

models. While Transformers are data-hungry, we hypothesize that our optimized framework could

mitigate their optimization instability on small datasets. The roadmap involves:

a) Benchmarking: Training baseline Transformer models on the Yoruba corpus using standard Adam.

b) Enhancement: Applying the Enhanced Adam framework to the same architectures.

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c) Analysis: Rigorously comparing the training stability, convergence speed, and final performance against

both the standard Adam baseline and the best-performing RNN-Attention model from this work. Success

here would significantly broaden the impact of our optimizer enhancements.

1. Cross-Lingual Transfer: Investigate whether an optimizer tuned on one low-resource language (like

Yoruba) can provide a "plug-and-play" performance boost when transferred to another morphologically

similar, low-resource language, reducing the need for language-specific optimizer tuning.

2. Theoretical Analysis: Develop a more rigorous theoretical understanding of why adaptive methods like

Adam behave pathologically on small datasets and how specific interventions like gradient clipping and

dynamic scheduling alter the optimization trajectory in the low-resource regime.

In conclusion, by treating the optimizer as a first-class object of research, we can unlock significant

performance gains and build more reliable and effective NLP tools for the world's underserved languages. The

roadmap outlined above provides a clear pathway for extending the contributions of this work towards more

complex architectures and a broader linguistic scope.

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