Papers with data and models
Where Do People Tell Stories Online? Story Detection Across Online Communities (2024.acl-long)
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| Challenge: | Story detection in online communities is a challenging task as stories are scattered across communities and interwoven with non-storytelling spans within a single text. |
| Approach: | They propose a toolkit to detect stories in online communities using an annotated reddit dataset and a codebook adapted to social media context. |
| Outcome: | The proposed toolkit includes an annotation-rich dataset of 502 Reddit posts and comments . it also includes a codebook adapted to the social media context and models to predict storytelling at document and span levels. |
IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages (2020.findings-emnlp)
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Divyanshu Kakwani, Anoop Kunchukuttan, Satish Golla, Gokul N.C., Avik Bhattacharyya, Mitesh M. Khapra, Pratyush Kumar
| Challenge: | In this paper, we present NLP resources for 11 major Indian languages . distributional representations are the cornerstone of modern NLP, authors say . |
| Approach: | They introduce NLP resources for 11 major Indian languages from two major language families . monolingual corpora contains 8.8 billion tokens across all 11 languages and Indian English . they also compile a benchmark for Indian language NLU to evaluate their results . |
| Outcome: | The monolingual corpora contains 8.8 billion tokens across all 11 languages and Indian English . the pre-trained language models are based on the compact ALBERT model . |
Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview (2020.acl-main)
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| Challenge: | a growing number of studies address the effect of bias on predictions, but no unifying framework exists . a general phenomenon of biased predictive models in NLP is not recent, authors say . |
| Approach: | They propose a unifying framework for identifying and reducing bias in natural language processing . they propose to differentiate two consequences of bias and four potential origins of bias . |
| Outcome: | The proposed framework provides an overview of predictive bias in natural language processing . it differentiates two consequences of bias and four potential origins of bias: label bias, selection bias, model overamplification, and semantic bias. |
Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages (2023.acl-long)
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Sumanth Doddapaneni, Rahul Aralikatte, Gowtham Ramesh, Shreya Goyal, Mitesh M. Khapra, Anoop Kunchukuttan, Pratyush Kumar
| Challenge: | Recent advances in Natural Language Understanding are driven by pretrained multilingual models, which can potentially reduce the performance gap between high-resource languages through zero-shot knowledge transfer. |
| Approach: | They propose to create a human-supervised benchmark for Indic languages, IndicXTREME, with nine diverse NLU tasks covering 20 languages. |
| Outcome: | The proposed model improves on the monolingual corpora, IndicCorp, and IndicBERT in Indic languages with 105 evaluation sets across languages and tasks. |