Sandeep Mathias

@presidencyinversity.in

Assistant Professor, Department of Computer Science and Engineering
Presidency University

Sandeep Mathias


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https://researchid.co/lwsam

RESEARCH INTERESTS

Natural Language Processing
10

Scopus Publications

443

Scholar Citations

9

Scholar h-index

9

Scholar i10-index

Scopus Publications

  • PresiUniv at FinCausal 2025 Shared Task: Applying Fine-tuned Language Models to Explain Financial Cause and Effect with Zero-shot Learning
    Proceedings International Conference on Computational Linguistics Coling, 2025
  • ISEP_Presidency_University at MLSP 2024 Shared Task: Using GPT-3.5 to Generate Substitutes for Lexical Simplification
    Proceedings of the 19th Workshop on Innovative Use of Nlp for Building Educational Applications, 2024
  • Many Hands Make Light Work: Using Essay Traits to Automatically Score Essays
    Rahul Kumar, Sandeep Mathias, Sriparna Saha, Pushpak Bhattacharyya
    Naacl 2022 2022 Conference of the North American Chapter of the Association for Computational Linguistics Human Language Technologies Proceedings of the Conference, 2022
    Most research in the area of automatic essay grading (AEG) is geared towards scoring the essay holistically while there has also been little work done on scoring individual essay traits. In this paper, we describe a way to score essays using a multi-task learning (MTL) approach, where scoring the essay holistically is the primary task, and scoring the essay traits is the auxiliary task. We compare our results with a single-task learning (STL) approach, using both LSTMs and BiLSTMs. To find out which traits work best for different types of essays, we conduct ablation tests for each of the essay traits. We also report the runtime and number of training parameters for each system. We find that MTL-based BiLSTM system gives the best results for scoring the essay holistically, as well as performing well on scoring the essay traits. The MTL systems also give a speed-up of between 2.30 to 3.70 times the speed of the STL system, when it comes to scoring the essay and all the traits.
  • Can neural networks automatically score Essay Traits?
    Sandeep Mathias, Pushpak Bhattacharyya
    Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2020
    Essay traits are attributes of an essay that can help explain how well written (or badly written) the essay is. Examples of traits include Content, Organization, Language, Sentence Fluency, Word Choice, etc. A lot of research in the last decade has dealt with automatic holistic essay scoring -where a machine rates an essay and gives a score for the essay. However, writers need feedback, especially if they want to improve their writing -which is why traitscoring is important. In this paper, we show how a deep-learning based system can outperform feature-based machine learning systems, as well as a string kernel system in scoring essay traits.
  • A survey on using gaze behaviour for natural language processing
    Ijcai International Joint Conference on Artificial Intelligence, 2020
  • Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze Behaviour
    Sandeep Mathias, Rudra Murthy, Diptesh Kanojia, Abhijit Mishra, Pushpak Bhattacharyya
    Proceedings of the 1st Conference of the Asia Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing Aacl Ijcnlp 2020, 2020
    Sandeep Mathias, Rudra Murthy, Diptesh Kanojia, Abhijit Mishra, Pushpak Bhattacharyya. Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing. 2020.
  • Thank “Goodness”! A Way to Measure Style in Student Essays
    Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2018
  • ASAP++: Enriching the ASAP automated essay grading dataset with essay attribute scores
    Lrec 2018 11th International Conference on Language Resources and Evaluation, 2018
  • The whole is greater than the sum of its parts: Towards the effectiveness of voting ensemble classifiers for complex word identification
    Nikhil Wani, Sandeep Mathias, Jayashree Aanand Gajjam, Pushpak Bhattacharyya
    Proceedings of the 13th Workshop on Innovative Use of Nlp for Building Educational Applications Bea 2018 at the 2018 Conference of the North American Chapter of the Association for Computational Linguistics Human Language Technologies Naacl Htl 2018, 2018
    In this paper, we present an effective system using voting ensemble classifiers to detect contextually complex words for non-native English speakers. To make the final decision, we channel a set of eight calibrated classifiers based on lexical, size and vocabulary features and train our model with annotated datasets collected from a mixture of native and nonnative speakers. Thereafter, we test our system on three datasets namely NEWS, WIKINEWS, and WIKIPEDIA and report competitive results with an F1-Score ranging between 0.777 to 0.855 for each of the datasets. Our system outperforms multiple other models and falls within 0.042 to 0.026 percent of the bestperforming model's score in the shared task.
  • Eyes are the windows to the soul: Predicting the rating of text quality using gaze behaviour
    Sandeep Mathias, Diptesh Kanojia, Kevin Patel, Samarth Agrawal, Abhijit Mishra, Pushpak Bhattacharyya
    Acl 2018 56th Annual Meeting of the Association for Computational Linguistics Proceedings of the Conference Long Papers, 2018
    Predicting a reader’s rating of text quality is a challenging task that involves estimating different subjective aspects of the text, like structure, clarity, etc. Such subjective aspects are better handled using cognitive information. One such source of cognitive information is gaze behaviour. In this paper, we show that gaze behaviour does indeed help in effectively predicting the rating of text quality. To do this, we first we model text quality as a function of three properties - organization, coherence and cohesion. Then, we demonstrate how capturing gaze behaviour helps in predicting each of these properties, and hence the overall quality, by reporting improvements obtained by adding gaze features to traditional textual features for score prediction. We also hypothesize that if a reader has fully understood the text, the corresponding gaze behaviour would give a better indication of the assigned rating, as opposed to partial understanding. Our experiments validate this hypothesis by showing greater agreement between the given rating and the predicted rating when the reader has a full understanding of the text.

RECENT SCHOLAR PUBLICATIONS

  • PresiUniv at FinCausal 2025 Shared Task: Applying fine-tuned language models to explain financial cause and effect with zero-shot learning
    M Jeenoor, M Aziz, SD Vaidyanathan, A Samantraya, S Mathias
    Proceedings of the Joint Workshop of the 9th Financial Technology and … , 2025
    2025
    Citations: 1
  • Isep_presidency_university at mlsp 2024 shared task: Using gpt-3.5 to generate substitutes for lexical simplification
    B Dutilleul, M Debaillon, S Mathias
    Proceedings of the 19th Workshop on Innovative Use of NLP for Building … , 2024
    2024
    Citations: 3
  • PresiUniv at TSAR-2022 shared task: Generation and ranking of simplification substitutes of complex words in multiple languages
    P Whistely, S Mathias, G Poornima
    Proceedings of the Workshop on Text Simplification, Accessibility, and … , 2022
    2022
    Citations: 14
  • Many Hands Make Light Work: Using Essay Traits to Automatically Score Essays
    R Kumar, S Mathias, S Saha, P Bhattacharyya
    2022 Conference of the North American Chapter of the Association for … , 2022
    2022
    Citations: 66
  • Cognitively Aided Zero-Shot Automatic Essay Grading
    S Mathias, R Murthy, D Kanojia, P Bhattacharyya
    International Conference on Natural Language Processing, 175 - 180 , 2020
    2020
    Citations: 5
  • Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze Behaviour
    S Mathias, R Murthy, D Kanojia, A Mishra, P Bhattacharyya
    1st Conference of the Asia-Pacific Chapter of the Association for … , 2020
    2020
    Citations: 17
  • Can Neural Networks Automatically Score Essay Traits?
    S Mathias, P Bhattacharyya
    15th Workshop on Innovative Use of NLP for Building Educational Applications … , 2020
    2020
    Citations: 73
  • A Survey on Using Gaze Behaviour for Natural Language Processing
    S Mathias, D Kanojia, A Mishra, P Bhattacharyya
    Twenty-Ninth International Joint Conference on Artificial Intelligence … , 2020
    2020
    Citations: 41
  • Eyes are the Windows to the Soul: Predicting the Rating of Text Quality Using Gaze Behaviour
    S Mathias, D Kanojia, K Patel, S Agrawal, A Mishra, P Bhattacharyya
    56th Annual Meeting of the Association for Computational Linguistics, 2352-2362 , 2018
    2018
    Citations: 28
  • Thank “goodness”! a way to measure style in student essays
    S Mathias, P Bhattacharyya
    Proceedings of the 5th Workshop on Natural Language Processing Techniques … , 2018
    2018
    Citations: 26
  • The whole is greater than the sum of its parts: Towards the effectiveness of voting ensemble classifiers for complex word identification
    N Wani, S Mathias, JA Gajjam, P Bhattacharyya
    Proceedings of the thirteenth workshop on innovative use of NLP for building … , 2018
    2018
    Citations: 16
  • ASAP++: Enriching the ASAP automated essay grading dataset with essay attribute scores
    S Mathias, P Bhattacharyya
    Proceedings of the eleventh international conference on language resources … , 2018
    2018
    Citations: 148
  • Using Machine Translation Evaluation Techniques to Evaluate Text Simplification Systems
    S Mathias, P Bhattacharyya
    Proceedings of the Quality Assessment for Text Simplification (QATS), 38-41 , 2016
    2016
    Citations: 2
  • How Hard Can it Be? The E-Score - A Scoring Metric to Assess the Complexity of Text
    S Mathias, P Bhattacharyya
    Proceedings of the Quality Assessment for Text Simplification (QATS), 10-14 , 2016
    2016
    Citations: 3

MOST CITED SCHOLAR PUBLICATIONS

  • ASAP++: Enriching the ASAP automated essay grading dataset with essay attribute scores
    S Mathias, P Bhattacharyya
    Proceedings of the eleventh international conference on language resources … , 2018
    2018
    Citations: 148
  • Can Neural Networks Automatically Score Essay Traits?
    S Mathias, P Bhattacharyya
    15th Workshop on Innovative Use of NLP for Building Educational Applications … , 2020
    2020
    Citations: 73
  • Many Hands Make Light Work: Using Essay Traits to Automatically Score Essays
    R Kumar, S Mathias, S Saha, P Bhattacharyya
    2022 Conference of the North American Chapter of the Association for … , 2022
    2022
    Citations: 66
  • A Survey on Using Gaze Behaviour for Natural Language Processing
    S Mathias, D Kanojia, A Mishra, P Bhattacharyya
    Twenty-Ninth International Joint Conference on Artificial Intelligence … , 2020
    2020
    Citations: 41
  • Eyes are the Windows to the Soul: Predicting the Rating of Text Quality Using Gaze Behaviour
    S Mathias, D Kanojia, K Patel, S Agrawal, A Mishra, P Bhattacharyya
    56th Annual Meeting of the Association for Computational Linguistics, 2352-2362 , 2018
    2018
    Citations: 28
  • Thank “goodness”! a way to measure style in student essays
    S Mathias, P Bhattacharyya
    Proceedings of the 5th Workshop on Natural Language Processing Techniques … , 2018
    2018
    Citations: 26
  • Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze Behaviour
    S Mathias, R Murthy, D Kanojia, A Mishra, P Bhattacharyya
    1st Conference of the Asia-Pacific Chapter of the Association for … , 2020
    2020
    Citations: 17
  • The whole is greater than the sum of its parts: Towards the effectiveness of voting ensemble classifiers for complex word identification
    N Wani, S Mathias, JA Gajjam, P Bhattacharyya
    Proceedings of the thirteenth workshop on innovative use of NLP for building … , 2018
    2018
    Citations: 16
  • PresiUniv at TSAR-2022 shared task: Generation and ranking of simplification substitutes of complex words in multiple languages
    P Whistely, S Mathias, G Poornima
    Proceedings of the Workshop on Text Simplification, Accessibility, and … , 2022
    2022
    Citations: 14
  • Cognitively Aided Zero-Shot Automatic Essay Grading
    S Mathias, R Murthy, D Kanojia, P Bhattacharyya
    International Conference on Natural Language Processing, 175 - 180 , 2020
    2020
    Citations: 5
  • Isep_presidency_university at mlsp 2024 shared task: Using gpt-3.5 to generate substitutes for lexical simplification
    B Dutilleul, M Debaillon, S Mathias
    Proceedings of the 19th Workshop on Innovative Use of NLP for Building … , 2024
    2024
    Citations: 3
  • How Hard Can it Be? The E-Score - A Scoring Metric to Assess the Complexity of Text
    S Mathias, P Bhattacharyya
    Proceedings of the Quality Assessment for Text Simplification (QATS), 10-14 , 2016
    2016
    Citations: 3
  • Using Machine Translation Evaluation Techniques to Evaluate Text Simplification Systems
    S Mathias, P Bhattacharyya
    Proceedings of the Quality Assessment for Text Simplification (QATS), 38-41 , 2016
    2016
    Citations: 2
  • PresiUniv at FinCausal 2025 Shared Task: Applying fine-tuned language models to explain financial cause and effect with zero-shot learning
    M Jeenoor, M Aziz, SD Vaidyanathan, A Samantraya, S Mathias
    Proceedings of the Joint Workshop of the 9th Financial Technology and … , 2025
    2025
    Citations: 1