Papers by Ashim Gupta
Whispers of Doubt Amidst Echoes of Triumph in NLP Robustness (2024.naacl-long)
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| Challenge: | Existing approaches to measure robustness are problematic, and out-of-domain evaluations are no longer relevant. |
| Approach: | They examine models of different sizes spanning different architectural choices and pretraining objectives. |
| Outcome: | The results show that not all out-of-domain tests provide insight into robustness . merely scaling models does not make them adequately robust . |
Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM Compression (2024.findings-emnlp)
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| Challenge: | Prior work on compression prioritizes preserving perplexity, which is analogous to training loss. |
| Approach: | They examine the impact of model compression along four dimensions: degeneration harm, representational harm, dialect bias, and language modeling and downstream task performance. |
| Outcome: | The proposed compression methods can lead to unexpected consequences, the authors show . quantization preserves bias while pruning degrades quickly. |
Keep it Surprisingly Simple: A Simple First Order Graph Based Parsing Model for Joint Morphosyntactic Parsing in Sanskrit (2020.emnlp-main)
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| Challenge: | Morphologically rich languages benefit from joint processing of morphology and syntax, as compared to pipeline architectures. |
| Approach: | They propose a graph-based model for joint morphological parsing and dependency parser in Sanskrit using the Energy based model framework. |
| Outcome: | The proposed model outperforms standalone morphological parsers in morphology and syntax parsing, and in dependency parser. |
A Little Pretraining Goes a Long Way: A Case Study on Dependency Parsing Task for Low-resource Morphologically Rich Languages (2021.eacl-srw)
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| Challenge: | Neural dependency parsing has been a success for many domains and languages, but the bottleneck of massive labelled data limits its effectiveness for low resource languages. |
| Approach: | They propose to use morphological knowledge to improve dependency parsing for morphology rich languages in a low-resource setting to perform experiments. |
| Outcome: | The proposed method achieves an average gain of 2 points (UAS) and 3.6 points (LAS) on 10 MRLs in low-resource settings. |
IntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery (2023.findings-emnlp)
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| Challenge: | Intent-related tasks are typically modeled as separate tasks, but a unified approach is proposed . INTENDD uses an entirely unsupervised contrastive learning strategy for representation learning . |
| Approach: | They propose a unified approach to identifying intents from dialogue utterances . they propose an unsupervised contrastive learning strategy for representation learning . |
| Outcome: | The proposed approach outperforms baselines on three intent-related tasks on multiple datasets. |
Does Meta-learning Help mBERT for Few-shot Question Generation in a Cross-lingual Transfer Setting for Indic Languages? (2022.coling-1)
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Aniruddha Roy, Rupak Kumar Thakur, Isha Sharma, Ashim Gupta, Amrith Krishna, Sudeshna Sarkar, Pawan Goyal
| Challenge: | Existing approaches to few-shot Question Generation (QG) are limited and require manual annotation. |
| Approach: | They propose to use multilingual BERT to perform few-shot question generation with cross-lingual transfer. |
| Outcome: | The proposed model improves in few-shot QG and human evaluation confirms it. |
X-Fact: A New Benchmark Dataset for Multilingual Fact Checking (2021.acl-short)
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| Challenge: | Several fact-checking initiatives, such as PolitiFact, expend manual labor to investigate and determine the truthfulness of viral statements. |
| Approach: | They propose a multilingual dataset for factual verification of naturally existing claims . they use a benchmark to evaluate the multilingual models . |
| Outcome: | The proposed model achieves an F-score of around 40%, suggesting it is a challenging benchmark for multilingual fact-checking models. |
Don’t Retrain, Just Rewrite: Countering Adversarial Perturbations by Rewriting Text (2023.acl-long)
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| Challenge: | ATINTER model can be used to rewrite adversarial inputs to make them non-adversarial . if undefended, model should maintain good task performance and effectively mitigate adversarials . |
| Approach: | They propose a model that intercepts adversarial inputs and learns to rewrite them . they show that it provides better adversarial robustness than existing defense approaches . |
| Outcome: | The proposed model improves adversarial robustness without compromising task accuracy on a sentiment classification dataset. |
Samayik: A Benchmark and Dataset for English-Sanskrit Translation (2024.lrec-main)
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Ayush Maheshwari, Ashim Gupta, Amrith Krishna, Atul Kumar Singh, Ganesh Ramakrishnan, Anil Kumar Gourishetty, Jitin Singla
| Challenge: | Existing Sanskrit corpora focus on poetry and offer limited coverage of contemporary written materials. |
| Approach: | They release a dataset of 53,000 parallel English-Sanskrit sentences . they use spoken content that covers contemporary world affairs and interpretations . |
| Outcome: | a new dataset of 53,000 parallel English-Sanskrit sentences is released . the dataset outperforms existing models trained on older classical-era poetry datasets . |