Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Simple or Complex? Learning to Predict Readability of Bengali Texts
Authors: Susmoy Chakraborty, Mir Tafseer Nayeem, Wasi Uddin Ahmad12621-12629
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We use our document level dataset to experiment with formula-based approaches and use the sentence-level dataset to train supervised neural models. ... We present the detailed ablation experiment results of our test set in Table 4. |
| Researcher Affiliation | Academia | Susmoy Chakraborty1*, Mir Tafseer Nayeem1*, Wasi Uddin Ahmad2 1Ahsanullah University of Science and Technology 2University of California, Los Angeles |
| Pseudocode | Yes | Algorithm 1: Consonant Conjunct Count Algorithm. |
| Open Source Code | Yes | We make our code & dataset publicly available at https://github. com/tafseer-nayeem/Bengali Readability for reproduciblity. |
| Open Datasets | Yes | We make our code & dataset publicly available at https://github. com/tafseer-nayeem/Bengali Readability for reproduciblity. ... We present several human-annotated corpora and dictionaries such as a document-level dataset comprising 618 documents with 12 different grade levels, a large-scale sentence-level dataset comprising more than 96K sentences with simple and complex labels... |
| Dataset Splits | Yes | Table 2: Statistics of the sentence-level dataset. ... Train Dev Test Simple Sentences #Sents 37,902 1,100 1,100 |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used (e.g., GPU/CPU models, memory) to run its experiments. |
| Software Dependencies | No | The paper mentions software like the BNLP library and iNLTK library, but does not provide specific version numbers for these or any other ancillary software dependencies required for reproducibility. |
| Experiment Setup | Yes | We use 60 as maximum sequence length with a batch size of 16, embedding size of 300, 64 LSTM hidden units, and Adam optimizer (Kingma and Ba 2015) with a learning rate of 0.001. We run the training for 50 epochs and check the improvement of validation (dev set) loss to save the latest best model during training. |