Search Results for author:
Found 15 papers, 6 papers with code
Does Meta-learning Help mBERT for Few-shot Question Generation in a Cross-lingual Transfer Setting for Indic Languages?
Further, we apply the model agnostic meta-learning approach to our base model.
Keep it Surprisingly Simple: A Simple First Order Graph Based Parsing Model for Joint Morphosyntactic Parsing in Sanskrit
We propose a graph-based model for joint morphological parsing and dependency parsing in Sanskrit.
A Graph-Based Framework for Structured Prediction Tasks in Sanskrit
Ours is a search-based structured prediction framework, which expects a graph as input, where relevant linguistic information is encoded in the nodes, and the edges are then used to indicate the association between these nodes.
Whispers of Doubt Amidst Echoes of Triumph in NLP Robustness
Do larger and more performant models resolve NLP's longstanding robustness issues?
IntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery
Further, the intent classification may be modeled in a multiclass (MC) or multilabel (ML) setup.
Adversarial Clean Label Backdoor Attacks and Defenses on Text Classification Systems
Clean-label (CL) attack is a form of data poisoning attack where an adversary modifies only the textual input of the training data, without requiring access to the labeling function.
Don't Retrain, Just Rewrite: Countering Adversarial Perturbations by Rewriting Text
For example, on sentiment classification using the SST-2 dataset, our method improves the adversarial accuracy over the best existing defense approach by more than 4% with a smaller decrease in task accuracy (0. 5% vs 2. 5%).
Sāmayik: A Benchmark and Dataset for English-Sanskrit Translation
Translation models trained on our dataset demonstrate statistically significant improvements when translating out-of-domain contemporary corpora, outperforming models trained on older classical-era poetry datasets.
Evaluating Relaxations of Logic for Neural Networks: A Comprehensive Study
Symbolic knowledge can provide crucial inductive bias for training neural models, especially in low data regimes.
X-FACT: A New Benchmark Dataset for Multilingual Fact Checking
In this work, we introduce X-FACT: the largest publicly available multilingual dataset for factual verification of naturally existing real-world claims.
A Little Pretraining Goes a Long Way: A Case Study on Dependency Parsing Task for Low-resource Morphologically Rich Languages
In this work, we focus on dependency parsing for morphological rich languages (MRLs) in a low-resource setting.
BERT & Family Eat Word Salad: Experiments with Text Understanding
In this paper, we study the response of large models from the BERT family to incoherent inputs that should confuse any model that claims to understand natural language.
Evaluating Neural Morphological Taggers for Sanskrit
Neural sequence labelling approaches have achieved state of the art results in morphological tagging.
Neural Approaches for Data Driven Dependency Parsing in Sanskrit
We compare the performance of each of the models in a low-resource setting, with 1, 500 sentences for training.
An LSTM-CRF Based Approach to Token-Level Metaphor Detection
In this paper, we propose a method for detection of metaphors at the token level using a hybrid model of Bidirectional-LSTM and CRF.
