Manoj Apte


2024

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Why Generate When You Can Discriminate? A Novel Technique for Text Classification using Language Models
Sachin Pawar | Nitin Ramrakhiyani | Anubhav Sinha | Manoj Apte | Girish Palshikar
Findings of the Association for Computational Linguistics: EACL 2024

In this paper, we propose a novel two-step technique for text classification using autoregressive Language Models (LM). In the first step, a set of perplexity and log-likelihood based numeric features are elicited from an LM for a text instance to be classified. Then, in the second step, a classifier based on these features is trained to predict the final label. The classifier used is usually a simple machine learning classifier like Support Vector Machine (SVM) or Logistic Regression (LR) and it is trained using a small set of training examples. We believe, our technique presents a whole new way of exploiting the available training instances, in addition to the existing ways like fine-tuning LMs or in-context learning. Our approach stands out by eliminating the need for parameter updates in LMs, as required in fine-tuning, and does not impose limitations on the number of training examples faced while building prompts for in-context learning. We evaluate our technique across 5 different datasets and compare with multiple competent baselines.

2023

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Audit Report Coverage Assessment using Sentence Classification
Sushodhan Vaishampayan | Nitin Ramrakhiyani | Sachin Pawar | Aditi Pawde | Manoj Apte | Girish Palshikar
Proceedings of the Sixth Workshop on Financial Technology and Natural Language Processing

Audit reports are a window to the financial health of a company and hence gauging coverage of various audit aspects in them is important. In this paper, we aim at determining an audit report’s coverage through classification of its sentences into multiple domain specific classes. In a weakly supervised setting, we employ a rule-based approach to automatically create training data for a BERT-based multi-label classifier. We then devise an ensemble to combine both the rule based and classifier approaches. Further, we employ two novel ways to improve the ensemble’s generalization: (i) through an active learning based approach and, (ii) through a LLM based review. We demonstrate that our proposed approaches outperform several baselines. We show utility of the proposed approaches to measure audit coverage on a large dataset of 2.8K audit reports.

2016

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Learning to Identify Subjective Sentences
Girish K. Palshikar | Manoj Apte | Deepak Pandita | Vikram Singh
Proceedings of the 13th International Conference on Natural Language Processing