MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education

Since the introduction of the original BERT (i.e., BASE BERT), researchers have developed various customized BERT models with improved performance for specific domains and tasks by exploiting the benefits of transfer learning. Due to the nature of mathematical texts, which often use domain specific vocabulary along with equations and math symbols, we posit that the development of a new BERT model for mathematics would be useful for many mathematical downstream tasks. In this resource paper, we introduce our multi-institutional effort (i.e., two learning platforms and three academic institutions in the US) toward this need: MathBERT, a model created by pre-training the BASE BERT model on a large mathematical corpus ranging from pre-kindergarten (pre-k), to high-school, to college graduate level mathematical content. In addition, we select three general NLP tasks that are often used in mathematics education: prediction of knowledge component, auto-grading open-ended Q&A, and knowledge tracing, to demonstrate the superiority of MathBERT over BASE BERT. Our experiments show that MathBERT outperforms prior best methods by 1.2-22% and BASE BERT by 2-8% on these tasks. In addition, we build a mathematics specific vocabulary 'mathVocab' to train with MathBERT. We discover that MathBERT pre-trained with 'mathVocab' outperforms MathBERT trained with the BASE BERT vocabulary (i.e., 'origVocab'). MathBERT is currently being adopted at the participated leaning platforms: Stride, Inc, a commercial educational resource provider, and ASSISTments.org, a free online educational platform. We release MathBERT for public usage at: https://github.com/tbs17/MathBERT.Comment: Accepted by NeurIPS 2021 MATHAI4ED Workshop (Best Paper

Deep learning based Arabic short answer grading in serious games

Automatic short answer grading (ASAG) has become part of natural language processing problems. Modern ASAG systems start with natural language preprocessing and end with grading. Researchers started experimenting with machine learning in the preprocessing stage and deep learning techniques in automatic grading for English. However, little research is available on automatic grading for Arabic. Datasets are important to ASAG, and limited datasets are available in Arabic. In this research, we have collected a set of questions, answers, and associated grades in Arabic. We have made this dataset publicly available. We have extended to Arabic the solutions used for English ASAG. We have tested how automatic grading works on answers in Arabic provided by schoolchildren in 6th grade in the context of serious games. We found out those schoolchildren providing answers that are 5.6 words long on average. On such answers, deep learning-based grading has achieved high accuracy even with limited training data. We have tested three different recurrent neural networks for grading. With a transformer, we have achieved an accuracy of 95.67%. ASAG for school children will help detect children with learning problems early. When detected early, teachers can solve learning problems easily. This is the main purpose of this research

A Trustworthy Automated Short-Answer Scoring System Using a New Dataset and Hybrid Transfer Learning Method

To measure the quality of student learning, teachers must conduct evaluations. One of the most efficient modes of evaluation is the short answer question. However, there can be inconsistencies in teacher-performed manual evaluations due to an excessive number of students, time demands, fatigue, etc. Consequently, teachers require a trustworthy system capable of autonomously and accurately evaluating student answers. Using hybrid transfer learning and student answer dataset, we aim to create a reliable automated short answer scoring system called Hybrid Transfer Learning for Automated Short Answer Scoring (HTL-ASAS). HTL-ASAS combines multiple tokenizers from a pretrained model with the bidirectional encoder representations from transformers. Based on our evaluation of the training model, we determined that HTL-ASAS has a higher evaluation accuracy than models used in previous studies. The accuracy of HTL-ASAS for datasets containing responses to questions pertaining to introductory information technology courses reaches 99.6%. With an accuracy close to one hundred percent, the developed model can undoubtedly serve as the foundation for a trustworthy ASAS system

The Influence of Variance in Learner Answers on Automatic Content Scoring

Automatic content scoring is an important application in the area of automatic educational assessment. Short texts written by learners are scored based on their content while spelling and grammar mistakes are usually ignored. The difficulty of automatically scoring such texts varies according to the variance within the learner answers. In this paper, we first discuss factors that influence variance in learner answers, so that practitioners can better estimate if automatic scoring might be applicable to their usage scenario. We then compare the two main paradigms in content scoring: (i) similarity-based and (ii) instance-based methods, and discuss how well they can deal with each of the variance-inducing factors described before

Automatic Short Answer Grading Using Transformers

RÉSUMÉ : L’évaluation des réponses courtes en langage naturel est une tendance dominante dans tout environnement éducatif. Ces techniques ont le potentiel d’aider les enseignants à mieux comprendre les réussites et les échecs de leurs élèves. En comparaison, les autres types d’évaluation ne mesurent souvent pas adéquatement les compétences des élèves, telles que les questions à choix multiples ou celles où il faut combler des espaces. Cependant, ce sont les moyens les plus fréquemment utilisés pour évaluer les élèves, en particulier dans les envi-ronnements de cours en ligne ouverts (MOOCs). La raison de leur emploi fréquent est que ces questions sont plus simples à corriger avec un ordinateur. Comparativement, devoir com-prendre et noter manuellement des réponses courtes est une tâche plus diÿcile et plus longue, d’autant plus en considérant le nombre croissant d’élèves en classe. La notation automatique de réponses courtes, généralement abrégée de l’anglais par ASAG, est une solution parfaite-ment adaptée à ce problème. Dans ce mémoire, nous nous concentrons sur le ASAG basé sur la classification avec des notes nominales, telles que correct ou incorrect. Nous proposons une approche par référence basée sur un modèle d’apprentissage profond, que nous entraînons sur quatre ensembles de données ASAG de pointe, à savoir SemEval-2013 (SciEntBank et BEETLE), Dt-grade et un jeu de données sur la biologie. Notre approche utilise les modèles BERT Base (sensible à la casse ou non) et XLNET Base (seulement sensible à la casse). Notre analyse subséquente emploie les ensembles de données GLUE (General Language Un-derstanding Evaluation), incluant des tâches de questions-réponses, d’implication textuelle, d’identification de paraphrases et d’analyse de similitude textuelle sémantique (STS). Nous démontrons que celles-ci contribuent à une meilleure performance des modèles sur la tâche ASAG, surtout avec le jeu de données SciEntBank.---------- ABSTRACT : Assessment of short natural language answers is a prevailing trend in any educational envi-ronment. It helps teachers to understand better the success and failure of students. Other types of questions such as multiple-choice or fill-in-the-gap questions don’t provide adequate clues for evaluating the students’ proficiency exhaustively. However, they are common means of student evaluation especially in Massive Open Online Courses (MOOCs) environments. One of the major reasons is that they are fairly easy to be graded. Nonetheless, understand-ing and marking manually short answers are more challenging and time-consuming tasks, especially when the number of students grows in a class. Automatic Short Answer Grading, usually abbreviated to ASAG, is a highly demanding solution in this current context. In this thesis, we mainly concentrate on classification-based ASAG with nominal grades such as correct or not correct. We propose a reference-based approach based on a deep learn-ing model on four ASAG state-of-the-art datasets, namely SemEval-2013 (SciEntBank and BEETLE), Dt-grade and Biology dataset. Our approach is based on BERT (cased and un-cased) and XLNET (cased) models. Our secondary analysis includes how GLUE (General Language Understanding Evaluation) tasks such as question answering, entailment, para-phrase identification and semantic textual similarity analysis strengthen the ASAG task on SciEntBank dataset. We show that language models based on transformers such as BERT and XLNET outperform or equal the state-of-the-art feature-based approaches. We further indicate that the performance of our BERT model increases substantially when we fine-tune a BERT model on an entailment task such as the GLUE MNLI dataset and then on the ASAG task compared to the other GLUE models

Neural approaches to discourse coherence: modeling, evaluation and application

Discourse coherence is an important aspect of text quality that refers to the way different textual units relate to each other. In this thesis, I investigate neural approaches to modeling discourse coherence. I present a multi-task neural network where the main task is to predict a document-level coherence score and the secondary task is to learn word-level syntactic features. Additionally, I examine the effect of using contextualised word representations in single-task and multi-task setups. I evaluate my models on a synthetic dataset where incoherent documents are created by shuffling the sentence order in coherent original documents. The results show the efficacy of my multi-task learning approach, particularly when enhanced with contextualised embeddings, achieving new state-of-the-art results in ranking the coherent documents higher than the incoherent ones (96.9%). Furthermore, I apply my approach to the realistic domain of people’s everyday writing, such as emails and online posts, and further demonstrate its ability to capture various degrees of coherence. In order to further investigate the linguistic properties captured by coherence models, I create two datasets that exhibit syntactic and semantic alterations. Evaluating different models on these datasets reveals their ability to capture syntactic perturbations but their inadequacy to detect semantic changes. I find that semantic alterations are instead captured by models that first build sentence representations from averaged word embeddings, then apply a set of linear transformations over input sentence pairs. Finally, I present an application for coherence models in the pedagogical domain. I first demonstrate that state of-the-art neural approaches to automated essay scoring (AES) are not robust to adversarially created, grammatical, but incoherent sequences of sentences. Accordingly, I propose a framework for integrating and jointly training a coherence model with a state-of-the-art neural AES system in order to enhance its ability to detect such adversarial input. I show that this joint framework maintains a performance comparable to the state-of-the-art AES system in predicting a holistic essay score while significantly outperforming it in adversarial detection