Navigating tenses in Bengali sentences: A stacked ensemble model for enhanced prediction

Umme Ayman; Md. Nahid Hasan; Ms. Nusrat Khan; Ms. Chayti Saha; Md. Fayejullah

doi:10.1371/journal.pone.0330186

Peer Review History

Original SubmissionJanuary 8, 2025
9 Apr 2025 Decision Letter - Namal Rathnayake, Editor PONE-D-24-57203Navigating Tenses in Bengali Sentences: A Stacked Ensemble Model for Enhanced PredictionPLOS ONE Dear Dr. Hasan, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by May 24 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. 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Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent. If data are owned by a third party, please indicate how others may request data access. 5. PLOS requires an ORCID iD for the corresponding author in Editorial Manager on papers submitted after December 6th, 2016. Please ensure that you have an ORCID iD and that it is validated in Editorial Manager. To do this, go to ‘Update my Information’ (in the upper left-hand corner of the main menu), and click on the Fetch/Validate link next to the ORCID field. This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager. Additional Editor Comments: Rewrite abstract and intro to specify task, dataset, model, metrics, and novelty. Add baseline comparisons, explain model architecture, handle class imbalance, discuss Bengali language challenges, interpretability, and justify the contribution clearly. Comments from PLOS Editorial Office: We note that one or more reviewers has recommended that you cite specific previously published works. As always, we recommend that you please review and evaluate the requested works to determine whether they are relevant and should be cited. It is not a requirement to cite these works. We appreciate your attention to this request. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Partly ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: No ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: No ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: No ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: Abstract needs complete rewrite. Add concrete info on model type, dataset used, accuracy achieved, and contribution. In intro, Clearly state the objective. Literature is not well synthesized. Review of recent deep learning NLP works for Bengali is missing. No clear novelty. Stacking models is not new. Authors must explicitly say: “Our novel contribution is…” and justify how this differs from prior Bengali NLP work. There is no detail on train-test split, feature preprocessing, or hyperparameter settings. Authors mention base learners but don’t explain architecture or why those were chosen. Provide diagrams or a clear pipeline. What metric is used for “highest accuracy” (line 91)? Is it F1? Precision? Add a limitations paragraph. Replace sentences like “paves the way for further research” (line 108) with real actionable outcomes. Avoid over-generalization. Needs professional proofreading. Read these studies to further enrich your study, (https://doi.org/10.1371/journal.pone.0312395) Supports your paper's socioeconomic perspective, especially how economic indicators shape inequality using ML. (https://doi.org/10.1371/journal.pone.0303883) Demonstrates use of ML with economic indicators in the Sri Lankan context, aligning with your economic modeling. (https://doi.org/10.1371/journal.pone.0282847) Reinforces your use of hybrid models like ANFIS and soft computing in Sri Lankan environmental data modeling. Reviewer #2: Navigating Tenses in Bengali Sentences: A Stacked Ensemble Model for Enhanced Prediction 1. You mention broad goals around improving accuracy and language modeling for Bengali, but you never define the specific task (e.g., classification? sentiment? named entity recognition?) in clear terms at the beginning. Define the target NLP task explicitly and connect it to your broader motivation, whether it's education, policy, or social impact in Bengali-speaking regions. 2. You claim your stacked model performs better, but there’s no comparison with a baseline model (e.g., logistic regression, SVM, or even a single deep model). This makes it impossible to assess whether the stacking architecture adds real value. Include results from simpler models to show performance gains. 3. There’s no mention of whether the dataset is balanced. If your dataset has skewed classes (which is common in real-world Bengali text), then accuracy alone is misleading. Did you use class weights, oversampling, or SMOTE? This should be discussed and reflected in metrics like F1 or recall per class. 4. Beyond noting the language, you don’t discuss the linguistic characteristics of Bengali that make this NLP task uniquely challenging (e.g., rich morphology, low- resource nature, ambiguous syntax). Add some language-specific insights to justify why your model is suitable and necessary. 5. Given that your stacked model is complex, how do you explain or interpret its predictions? Even a brief mention of SHAP values, attention weights, or feature importance would increase trust and make the model more actionable for policymakers or users in the region. 6. Refer to this work, relevant to your socio-contextual motivation of language- based emotion/sentiment classification in noisy settings. DOI: 10.1038/s41598-024-67269-2 ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0330186.r001
Revision 1
9 Jun 2025 Author Response Title: “Navigating Tenses in Bengali Sentences: A Stacked Ensemble Model for Enhanced Prediction” (Submission ID: PONE-D-24-57203) # Response to Reviewer 1: (Color code= Red) #Point 1: Abstract needs complete rewrite. Add concrete info on model type, dataset used, accuracy achieved, and contribution. Author Response: Thank you for your valuable and constructive feedback. In response to your comment, we have completely revised the abstract to clearly articulate the scope, methodology, and contributions of our work. The updated abstract now specifies the NLP task of Bengali tense classification and its relevance to downstream applications. We describe the construction of a novel dataset, “BengaliTenseCorpus,” comprising 13,500 manually labeled sentences across three tense categories. Additionally, we detail our proposed stacked ensemble model integrating five base classifiers (Random Forest, SVM, XGBoost, LSTM, and GRU) with a neural network meta-learner, and report the achieved test accuracy of 85%. We also highlight the novelty of combining machine learning and deep learning techniques for this task and outline future directions. These additions ensure the abstract conveys the model type, dataset used, accuracy obtained, and the overall contribution of our work, in line with your suggestion. Author Action: Based on the reviewer’s comments, we have updated our Abstract Section to meet the reviewer’s concerns. #Point 2: In intro, Clearly state the objective. Literature is not well synthesized. Review of recent deep learning NLP works for Bengali is missing. Author Response: Thank you for your thoughtful feedback. In response, we have revised the introduction to explicitly state the main objective of the study as developing a robust stacked ensemble model for automatic tense classification in Bengali sentences and its significance in the context of Bangla NLP. Additionally, we have strengthened the literature review by synthesizing existing works more cohesively and incorporating several recent studies that apply deep learning techniques to Bengali language tasks, such as classification, prediction, and detection. These additions provide a clearer context for our contribution and ensure that the background is both comprehensive and current. We believe these revisions directly address the reviewer’s concerns and enhance the clarity and relevance of the introduction. Author Action: Based on the reviewer’s comments, we have updated our Introduction Section and Literature Review Section to meet the reviewer’s concerns.#Point 3: No clear novelty. Stacking models is not new. Authors must explicitly say: “Our novel contribution is…” and justify how this differs from prior Bengali NLP work. Author Response: Thank you for your kind remarks. There is little to no work present in the predictive model to predict the tense of Bangla text. The sole reason for this problem is the lack of a valid dataset. Our novel contribution is to create a meaningful dataset for Bangla tense prediction and create a predictive model around it Author Action: We have included our novel contribution statement at the end of the introduction section according to the reviewer's suggestions. #Point 4: There is no detail on train test split, feature preprocessing, or hyperparameter settings. Author Response: Thank you for your valuable feedback. Author Action: With the renamed subsection of "Model Selection and Hyper Parameters," all information pertaining to the train-test split and hyper hyperparameter setup is added to the methodology section. Likewise, details regarding feature processing are included in the methodology part of the paragraph titled "Data Pre-processing". #Point 5: Authors mention base learners but don’t explain architecture or why those were chosen. Provide diagrams or a clear pipeline. Author Response: We selected base models based on their empirical performance during cross-validation. Each model demonstrated strong predictive ability individually, which makes them suitable candidates for contributing diverse and accurate predictions in the ensemble. Our ensemble model consists of machine learning models and deep learning models. These models use different learning biases. This diversity is essential in stacking to ensure the meta-learner can generalize well across prediction errors. A clear pipeline is presented in Figure 10. Author Action: A brief discussion of our preferred models while creating the ensemble model is given in “Our Model” subsection. #Point 6: What metric is used for “highest accuracy” (line 91)? Is it F1? Precision? Author Response: Thank you for your feedback. The accuracy score of such a model is referred to as the highest accuracy. For that model, the F1 score, precision, and recall were all 0.989.Author Action: We have rewritten the sentence accordingly. #Point 7: Add a Limitation Paragraph. Author Response: We appreciate reviewers' valuable insight. Author Action: A paragraph has been added at the end of the conclusion section to discuss the limitations and future work. #Point 8: Replace sentences like “paves the way for further research” (line 108) with real actionable outcomes. Avoid over generalization. Needs professional proofreading. Author Response: In response, we have revised overgeneralized statements such as “paves the way for further research” (line 108) and replaced them with specific, actionable outcomes related to dataset expansion, transformer-based model integration, and tense morphology conversion. Additionally, the entire manuscript has been carefully proofread and refined to improve clarity, precision, and professionalism in language. We believe these changes enhance both the quality and specificity of the manuscript. Author Action: Based on the reviewer’s comments, we have replaced the statement with constructive outcome. #Point 9: Read these studies to further enrich your study, (https://doi.org/10.1371/journal.pone.0312395) Supports your paper's socioeconomic perspective, especially how economic indicators shape inequality using ML. (https://doi.org/10.1371/ journal.pone.0303883) Demonstrates use of ML with economic indicators in the Sri Lankan context, aligning with your economic modeling. (https://doi.org/10.1371/journal.pone.0282847) Reinforces your use of hybrid models like ANFIS and soft computing in Sri Lankan environmental data modeling. Author Response: Thank you for recommending these valuable studies. We have reviewed all three articles and incorporated relevant insights into the manuscript to strengthen the socioeconomic and methodological context of our work. Specifically, we referenced the use of machine learning in economic inequality modeling and hybrid approaches like ANFIS in environmental data analysis, particularly in low-resource contexts similar to ours. These additions have enriched the background and discussion sections and helped to better position our contribution within existing literature. Author Action: Based on the reviewer’s comments, we have updated the literature review.# Response to Reviewer 2: (Color code= Green) #Point 1: You mention broad goals around improving accuracy and language modeling for Bengali, but you never define the specific task (e.g., classification? sentiment? named entity recognition?) in clear terms at the beginning. Define the target NLP task explicitly and connect it to your broader motivation, whether it's education, policy, or social impact in Bengali speaking regions. Author Response: In response, we have revised the introduction to clearly define the target NLP task as automatic tense classification in Bengali text. We have explicitly stated this task early in the introduction and connected it to broader motivations such as improving educational tools, supporting grammar correction, and enabling more inclusive and effective NLP technologies for Bengali-speaking communities. This clarification helps to better frame the scope and societal relevance of our work. Author Action: Updated the introduction to explicitly define the target NLP task as automatic tense classification and linked it to broader motivations such as educational support, grammar correction, and enhancing NLP applications for Bengali-speaking communities. #Point 2: You claim your stacked model performs better, but there’s no comparison with a baseline model (e.g., logistic regression, SVM, or even a single deep model). This makes it impossible to assess whether the stacking architecture adds real value. Include results from simpler models to show performance gains. Author Response: We thank the reviewers for their valuable insights. We included a detailed comparison table of several machine learning models (6 models) and deep learning (3 models) models' performance in the “Results and Discussion” section, with accuracy, recall, and F-1 scores. In every case, our proposed stacking ensemble model outperformed all the machine learning and deep learning models. Author Action: We included a paragraph stating the information in the “Proposed Combined Model” section.#Point 3: There’s no mention of whether the dataset is balanced. If your dataset has skewed classes (which is common in real world Bengali text), then accuracy alone is misleading. Did you use class weights, oversampling, or SMOTE? This should be discussed and reflected in metrics like F1 or recall per class. Author Response: Thank you for this valuable observation. Our dataset was balanced. For that reason, we didn’t require any oversampling techniques. The support value that reflects the per-class instance is present in every table for every model in the “Results and Discussion” section. Author Response: We have also included the dataset information about balancing in “Dataset Collection and Properties” subsection. #Point 4: Beyond noting the language, you don’t discuss the linguistic characteristics of Bengali that make this NLP task uniquely challenging (e.g., rich morphology, low resource nature, ambiguous syntax). Add some language specific insights to justify why your model is suitable and necessary. Author Response: Thank you for this valuable observation. In response, we have revised the introduction to include a detailed discussion of Bengali’s linguistic characteristics that make tense classification particularly challenging. Specifically, we address its rich inflectional morphology, low-resource nature, syntactic ambiguity, and the predominance of suffix-based tense marking. We also explain how our stacked ensemble model is well-suited to handle these complexities by combining deep learning for sequence modeling and machine learning for robust pattern recognition. These additions help clarify the necessity and relevance of our approach Author Action: We have Revised the introduction to include a detailed explanation of Bengali's linguistic features, such as its rich morphology, low-resource status, and syntactic ambiguity, and justified the suitability of the proposed stacked ensemble model in addressing these challenges. #Point 5: Given that your stacked model is complex, how do you explain or interpret its predictions? Even a brief mention of SHAP values, attention weights, or feature importance would increase trust and make the model more actionable for policymakers or users in the region. Author Response: We appreciate the reviewer’s emphasis on model interpretability, particularly given the complexity of our stacked ensemble and the importance of actionable insights for policymakers. While SHAP analysis is challenging to apply directly to our current ensemble due to the heterogeneous natureof the base models and their text processing pipelines, we agree that interpretability is critical for transparency and trust. In future work, we aim to incorporate token-level attribution methods such as SHAP. Author Action: Added as Future Work in the Conclusion section. #Point 6: Refer to this work, relevant to your socio contextual motivation of language based emotion/sentiment classification in noisy settings. DOI: 10.1038/s41598-024-67269-2 Author Response: In response, we have tried to reach this paper. We found a similar paper titled “Customer Sentiment Recognition in Conversation Based on Contextual Semantic and Affective Interaction Information” Author Action: We have included the paper in Literature Review section Again, thank you for your kind cooperation and consideration. Best Regards, Md. Nahid Hasan Corresponding Author Attachments Attachment Submitted filename: Response to Reviewers_PONE-D-24-57203.pdf https://doi.org/10.1371/journal.pone.0330186.r002
29 Jul 2025 Decision Letter - Saman Kasmaiee, Editor Navigating Tenses in Bengali Sentences: A Stacked Ensemble Model for Enhanced Prediction PONE-D-24-57203R1 Dear Dr. Hasan, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Saman Kasmaiee, Ph.D. Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #3: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #3: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #3: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #3: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #3: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #3: The authors have done a good job in addressing the raised issues. It would be great if they could address the issue of inter-annotator agreement score for the manual labelling and error analysis, and also compress teh "preprocessing" section. ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #3: No ******** https://doi.org/10.1371/journal.pone.0330186.r003
Formally Accepted
Acceptance Letter - Saman Kasmaiee, Editor PONE-D-24-57203R1 PLOS ONE Dear Dr. Hasan, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. You will receive an invoice from PLOS for your publication fee after your manuscript has reached the completed accept phase. If you receive an email requesting payment before acceptance or for any other service, this may be a phishing scheme. Learn how to identify phishing emails and protect your accounts at https://explore.plos.org/phishing. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Saman Kasmaiee Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0330186.r004

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