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Found 28 papers, 4 papers with code
A Graph Convolution Network-based System for Technical Domain Identification
This paper presents the IITKGP contribution at the Technical DOmain Identification (TechDOfication) shared task at ICON 2020.
A little perturbation makes a difference: Treebank augmentation by perturbation improves transfer parsing
We present an approach for cross-lingual transfer of dependency parser so that the parser trained on a single source language can more effectively cater to diverse target languages.
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.
Deciphering Political Entity Sentiment in News with Large Language Models: Zero-Shot and Few-Shot Strategies
Employing a chain-of-thought (COT) approach augmented with rationale in few-shot in-context learning, we assess whether this method enhances sentiment prediction accuracy.
Analyzing Sentiment Polarity Reduction in News Presentation through Contextual Perturbation and Large Language Models
In today's media landscape, where news outlets play a pivotal role in shaping public opinion, it is imperative to address the issue of sentiment manipulation within news text.
A Novel Multi-Stage Prompting Approach for Language Agnostic MCQ Generation using GPT
We introduce a multi-stage prompting approach (MSP) for the generation of multiple choice questions (MCQs), harnessing the capabilities of GPT models such as text-davinci-003 and GPT-4, renowned for their excellence across various NLP tasks.
Harnessing the Power of Prompt-based Techniques for Generating School-Level Questions using Large Language Models
We carefully annotate this dataset as quadruples of 1) Context: a segment upon which the question is formed; 2) Long Prompt: a long textual cue for the question (i. e., a longer sequence of words or phrases, covering the main theme of the context); 3) Short Prompt: a short textual cue for the question (i. e., a condensed representation of the key information or focus of the context); 4) Question: a deep question that aligns with the context and is coherent with the prompts.
Data-Driven Probabilistic Energy Consumption Estimation for Battery Electric Vehicles with Model Uncertainty
In this research article, we propose a new driver behaviour-centric EV energy consumption estimation model using probabilistic neural networks with model uncertainty.
'If you build they will come': Automatic Identification of News-Stakeholders to detect Party Preference in News Coverage
In contrast, the anti-government news agencies would focus more on the views of the opponent stakeholders to inform the readers about the shortcomings of government policies.
ArgFuse: A Weakly-Supervised Framework for Document-Level Event Argument Aggregation
Most of the existing information extraction frameworks (Wadden et al., 2019; Veysehet al., 2020) focus on sentence-level tasks and are hardly able to capture the consolidated information from a given document.
Event Argument Extraction using Causal Knowledge Structures
We develop a causal network for our event-annotated dataset by extracting relevant event causal structures from ConceptNet and phrases from Wikipedia.
A Robust Fuel Optimization Strategy For Hybrid Electric Vehicles: A Deep Reinforcement Learning Based Continuous Time Design Approach
This is followed by the design of a deep reinforcement learning based optimal control law for the non-linear system (i. e., hybrid electric vehicles) such that the actual states and the control policy remain close to the optimal trajectory and optimal policy even in the presence of external disturbances, modeling errors, uncertainties and noise.
Medical Entity Linking using Triplet Network
Entity linking (or Normalization) is an essential task in text mining that maps the entity mentions in the medical text to standard entities in a given Knowledge Base (KB).
Improving cross-lingual model transfer by chunking
We present a shallow parser guided cross-lingual model transfer approach in order to address the syntactic differences between source and target languages more effectively.
Biomedical Relation Classification by single and multiple source domain adaptation
Relation classification is crucial for inferring semantic relatedness between entities in a piece of text.
Delexicalized transfer parsing for low-resource languages using transformed and combined treebanks
This paper describes our dependency parsing system in CoNLL-2017 shared task on Multilingual Parsing from Raw Text to Universal Dependencies.
A study of attention-based neural machine translation model on Indian languages
Neural machine translation (NMT) models have recently been shown to be very successful in machine translation (MT).
Development of a Bengali parser by cross-lingual transfer from Hindi
A parser is trained and applied to the Hindi sentences of the parallel corpus and the parse trees are projected to construct probable parse trees of the corresponding Bengali sentences.
Query Translation for Cross-Language Information Retrieval using Multilingual Word Clusters
In Cross-Language Information Retrieval, finding the appropriate translation of the source language query has always been a difficult problem to solve.
UsingWord Embeddings for Query Translation for Hindi to English Cross Language Information Retrieval
In this paper, we propose an approach based on word embeddings, a method that captures contextual clues for a particular word in the source language and gives those words as translations that occur in a similar context in the target language.
