Search Results for author: Saumajit Saha

Found 7 papers, 1 papers with code

Stylistic MR-to-Text Generation Using Pre-trained Language Models

no code implementations ICON 2021 Kunal Pagarey, Kanika Kalra, Abhay Garg, Saumajit Saha, Mayur Patidar, Shirish Karande

We explore the ability of pre-trained language models BART, an encoder-decoder model, GPT2 and GPT-Neo, both decoder-only models for generating sentences from structured MR tags as input.

POS Sentence +1

Performance of BERT on Persuasion for Good

no code implementations ICON 2021 Saumajit Saha, Kanika Kalra, Manasi Patwardhan, Shirish Karande

We consider the task of automatically classifying the persuasion strategy employed by an utterance in a dialog.

A Multi-task Model for Multilingual Trigger Detection and Classification

no code implementations ICON 2019 Sovan Kumar Sahoo, Saumajit Saha, Asif Ekbal, Pushpak Bhattacharyya

In this paper we present a deep multi-task learning framework for multilingual event and argument trigger detection and classification.

Classification Multi-Task Learning

BanglaNLP at BLP-2023 Task 1: Benchmarking different Transformer Models for Violence Inciting Text Detection in Bengali

no code implementations16 Oct 2023 Saumajit Saha, Albert Nanda

This paper presents the system that we have developed while solving this shared task on violence inciting text detection in Bangla.

Benchmarking Data Augmentation +1

BanglaNLP at BLP-2023 Task 2: Benchmarking different Transformer Models for Sentiment Analysis of Bangla Social Media Posts

1 code implementation13 Oct 2023 Saumajit Saha, Albert Nanda

Bangla is the 7th most widely spoken language globally, with a staggering 234 million native speakers primarily hailing from India and Bangladesh.

Benchmarking Sentiment Analysis +2

A Platform for Event Extraction in Hindi

no code implementations LREC 2020 Sovan Kumar Sahoo, Saumajit Saha, Asif Ekbal, Pushpak Bhattacharyya

In this paper, we present an Event Extraction framework for Hindi language by creating an annotated resource for benchmarking, and then developing deep learning based models to set as the baselines.

Benchmarking Classification +2

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