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Found 60 papers, 25 papers with code
SyMCoM - Syntactic Measure of Code Mixing A Study Of English-Hindi Code-Mixing
Code mixing is the linguistic phenomenon where bilingual speakers tend to switch between two or more languages in conversations.
HashSet - A Dataset For Hashtag Segmentation
HashSet dataset is sampled from a different set of tweets when compared to existing datasets and provides an alternate distribution of hashtags to build and validate hashtag segmentation models.
CoMeT: Towards Code-Mixed Translation Using Parallel Monolingual Sentences
Code-mixed languages are very popular in multilingual societies around the world, yet the resources lag behind to enable robust systems on such languages.
An Exploratory Study on Temporally Evolving Discussion around Covid-19 using Diachronic Word Embeddings
In this work, we aim to show the topics under discussion, evolution of discussions, change in user sentiment during the pandemic.
Towards identifying Source credibility on Information Leakage in Digital Gadget Market
With the digital gadget market becoming highly competitive and ever-evolving, the trend of an increasing number of sensitive posts leaking information on devices in social media is observed.
Towards Infusing Auxiliary Knowledge for Distracted Driver Detection
Distracted driving is a leading cause of road accidents globally.
Multilingual Non-Factoid Question Answering with Silver Answers
The APS model attained an accuracy of 80\% and 72\%, as well as a macro F1 of 72\% and 66\%, on the MuNfQuAD testset and the golden set, respectively.
Counter Turing Test ($CT^2$): Investigating AI-Generated Text Detection for Hindi -- Ranking LLMs based on Hindi AI Detectability Index ($ADI_{hi}$)
The widespread adoption of large language models (LLMs) and awareness around multilingual LLMs have raised concerns regarding the potential risks and repercussions linked to the misapplication of AI-generated text, necessitating increased vigilance.
Higher Order Structures For Graph Explanations
Graph Neural Networks (GNNs) have emerged as powerful tools for learning representations of graph-structured data, demonstrating remarkable performance across various tasks.
Benchmarks Underestimate the Readiness of Multi-lingual Dialogue Agents
However, after manual evaluation of the validation set, we find that by correcting gold label errors and improving dataset annotation schema, GPT-4 with our prompts can achieve (1) 89. 6%-96. 8% accuracy in DST, and (2) more than 99% correct response generation across different languages.
From Human Judgements to Predictive Models: Unravelling Acceptability in Code-Mixed Sentences
To this end, we construct Cline - a dataset containing human acceptability judgements for English-Hindi (en-hi) code-mixed text.
Wu's Method can Boost Symbolic AI to Rival Silver Medalists and AlphaGeometry to Outperform Gold Medalists at IMO Geometry
In this note, we revisit the IMO-AG-30 Challenge introduced with AlphaGeometry, and find that Wu's method is surprisingly strong.
The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning
To measure these risks of malicious use, government institutions and major AI labs are developing evaluations for hazardous capabilities in LLMs.
COBIAS: Contextual Reliability in Bias Assessment
We develop the Context-Oriented Bias Indicator and Assessment Score (COBIAS) to measure a biased statement's reliability in detecting bias based on the variance in model behavior across different contexts.
SaGE: Evaluating Moral Consistency in Large Language Models
To this extent, we construct the Moral Consistency Corpus (MCC), containing 50K moral questions, responses to them by LLMs, and the RoTs that these models followed.
Multilingual Coreference Resolution in Low-resource South Asian Languages
We introduce a Translated dataset for Multilingual Coreference Resolution (TransMuCoRes) in 31 South Asian languages using off-the-shelf tools for translation and word-alignment.
Corrective Machine Unlearning
We hope our work spurs research towards developing better methods for corrective unlearning and offers practitioners a new strategy to handle data integrity challenges arising from web-scale training.
InSaAF: Incorporating Safety through Accuracy and Fairness | Are LLMs ready for the Indian Legal Domain?
Task performance and fairness scores of LLaMA and LLaMA--2 models indicate that the proposed $LSS_{\beta}$ metric can effectively determine the readiness of a model for safe usage in the legal sector.
Representation Surgery: Theory and Practice of Affine Steering
In the case of neural language models, an encoding of the undesirable behavior is often present in the model's representations.
RanDumb: A Simple Approach that Questions the Efficacy of Continual Representation Learning
However, little attention has been paid to the efficacy of continually learned representations, as representations are learned alongside classifiers throughout the learning process.
Measuring Moral Inconsistencies in Large Language Models
To address this issue, we propose a novel information-theoretic measure called Semantic Graph Entropy (SGE) to measure the consistency of an LLM in moral scenarios.
Towards Effective Paraphrasing for Information Disguise
Information Disguise (ID), a part of computational ethics in Natural Language Processing (NLP), is concerned with best practices of textual paraphrasing to prevent the non-consensual use of authors' posts on the Internet.
Exploring Graph Neural Networks for Indian Legal Judgment Prediction
We explored various embeddings as model features, while nodes such as time nodes and judicial acts were added and pruned to evaluate the model's performance.
X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents
We create a new multilingual benchmark, X-RiSAWOZ, by translating the Chinese RiSAWOZ to 4 languages: English, French, Hindi, Korean; and a code-mixed English-Hindi language.
CiteCaseLAW: Citation Worthiness Detection in Caselaw for Legal Assistive Writing
In legal document writing, one of the key elements is properly citing the case laws and other sources to substantiate claims and arguments.
CAFIN: Centrality Aware Fairness inducing IN-processing for Unsupervised Representation Learning on Graphs
Unsupervised Representation Learning on graphs is gaining traction due to the increasing abundance of unlabelled network data and the compactness, richness, and usefulness of the representations generated.
Are Models Trained on Indian Legal Data Fair?
We highlight the propagation of learnt algorithmic biases in the bail prediction task for models trained on Hindi legal documents.
Predictive linguistic cues for fake news: a societal artificial intelligence problem
Media news are making a large part of public opinion and, therefore, must not be fake.
PreCogIIITH at HinglishEval : Leveraging Code-Mixing Metrics & Language Model Embeddings To Estimate Code-Mix Quality
Code-Mixing is a phenomenon of mixing two or more languages in a speech event and is prevalent in multilingual societies.
FaIRCoP: Facial Image Retrieval using Contrastive Personalization
Retrieving facial images from attributes plays a vital role in various systems such as face recognition and suspect identification.
Learning to Automate Follow-up Question Generation using Process Knowledge for Depression Triage on Reddit Posts
We demonstrate the challenge of using existing datasets to train a DLM for generating FQs that adhere to clinical process knowledge.
Zero-shot Entity and Tweet Characterization with Designed Conditional Prompts and Contexts
In this work, we approach the problem of characterizing Named Entities and Tweets as an open-ended text classification and open-ended fact probing problem. We evaluate the zero-shot language model capabilities of Generative Pretrained Transformer 2 (GPT-2) to characterize Entities and Tweets subjectively with human psychology-inspired and logical conditional prefixes and contexts.
HLDC: Hindi Legal Documents Corpus
Further, as a use-case for the corpus, we introduce the task of bail prediction.
GAME-ON: Graph Attention Network based Multimodal Fusion for Fake News Detection
A plethora of previous multimodal-based work has tried to address the problem of modeling heterogeneous modalities in identifying fake content.
HashSet -- A Dataset For Hashtag Segmentation
HashSet dataset is sampled from a different set of tweets when compared to existing datasets and provides an alternate distribution of hashtags to build and validate hashtag segmentation models.
Towards Adversarial Evaluations for Inexact Machine Unlearning
Machine Learning models face increased concerns regarding the storage of personal user data and adverse impacts of corrupted data like backdoors or systematic bias.
Diagnosing Data from ICTs to Provide Focused Assistance in Agricultural Adoptions
Our classifier achieves accuracies ranging from 79% to 90% across the five states, demonstrating its potential for assisting future ethnographic investigations.
Battling Hateful Content in Indic Languages HASOC '21
We adopt a multilingual transformer based approach and describe our architecture for all 6 subtasks as part of the challenge.
Statistical Learning to Operationalize a Domain Agnostic Data Quality Scoring
The current empirical study was undertaken to formulate a concrete automated data quality platform to assess the quality of incoming dataset and generate a quality label, score and comprehensive report.
"Subverting the Jewtocracy": Online Antisemitism Detection Using Multimodal Deep Learning
Hence, we collect and label two datasets with 3, 102 and 3, 509 social media posts from Twitter and Gab respectively.
Factorization of Fact-Checks for Low Resource Indian Languages
The majority of studies on automatic fact-checking and fake news detection is restricted to English only.
Get It Scored Using AutoSAS -- An Automated System for Scoring Short Answers
In this paper, we present a fast, scalable, and accurate approach towards automated Short Answer Scoring (SAS).
(Un)Masked COVID-19 Trends from Social Media
Wearing masks is a useful protection method against COVID-19, which has caused widespread economic and social impact worldwide.
AbuseAnalyzer: Abuse Detection, Severity and Target Prediction for Gab Posts
While extensive popularity of online social media platforms has made information dissemination faster, it has also resulted in widespread online abuse of different types like hate speech, offensive language, sexist and racist opinions, etc.
imdpGAN: Generating Private and Specific Data with Generative Adversarial Networks
With experiments on MNIST dataset, we show that imdpGAN preserves the privacy of the individual data point, and learns latent codes to control the specificity of the generated samples.
Multi-objective Reinforcement Learning based approach for User-Centric Power Optimization in Smart Home Environments
We experiment on real-world smart home data, and show that the multi-objective approaches: i) establish trade-off between the two objectives, ii) achieve better combined user satisfaction and power consumption than single-objective approaches.
VacSIM: Learning Effective Strategies for COVID-19 Vaccine Distribution using Reinforcement Learning
We approach this problem by proposing a novel pipeline VacSIM that dovetails Deep Reinforcement Learning models into a Contextual Bandits approach for optimizing the distribution of COVID-19 vaccine.
Psychometric Analysis and Coupling of Emotions Between State Bulletins and Twitter in India during COVID-19 Infodemic
In this study, we analyze the psychometric impact and coupling of the COVID-19 infodemic with the official bulletins related to COVID-19 at the national and state level in India.
A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic
Conclusion: We conclude that a multi-pronged machine learning application delivering vernacular bite-sized audios and conversational AI is an effective approach to mitigate health misinformation.
Is change the only constant? Profile change perspective on #LokSabhaElections2019
Users on Twitter are identified with the help of their profile attributes that consists of username, display name, profile image, to name a few.
Hashtags are (not) judgemental: The untold story of Lok Sabha elections 2019
In the political context, hashtags on Twitter are used by users to campaign for their parties, spread news, or to get followers and get a general idea by following a discussion built around a hashtag.
What sets Verified Users apart? Insights, Analysis and Prediction of Verified Users on Twitter
The aim of the paper is two-fold: First, we test if discerning the verification status of a handle from profile metadata and content features is feasible.
Attentional Road Safety Networks
Road safety mapping using satellite images is a cost-effective but a challenging problem for smart city planning.
Neural Machine Translation for English-Tamil
We propose a novel neural machine translation technique using word-embedding along with Byte-Pair-Encoding (BPE) to develop an efficient translation system that overcomes the OOV (Out Of Vocabulary) problem for languages which do not have much translations available online.
Mind Your Language: Abuse and Offense Detection for Code-Switched Languages
In multilingual societies like the Indian subcontinent, use of code-switched languages is much popular and convenient for the users.
Language Identification and Named Entity Recognition in Hinglish Code Mixed Tweets
While growing code-mixed content on Online Social Networks(OSN) provides a fertile ground for studying various aspects of code-mixing, the lack of automated text analysis tools render such studies challenging.
A Twitter Corpus for Hindi-English Code Mixed POS Tagging
Code-mixing is a linguistic phenomenon where multiple languages are used in the same occurrence that is increasingly common in multilingual societies.
Hardening Deep Neural Networks via Adversarial Model Cascades
Deep neural networks (DNNs) are vulnerable to malicious inputs crafted by an adversary to produce erroneous outputs.
PicHunt: Social Media Image Retrieval for Improved Law Enforcement
This results in the need for first responders to inspect the spread of such images and users propagating them on social media.
Analyzing Social and Stylometric Features to Identify Spear phishing Emails
To the best of our knowledge, this is one of the first attempts to make use of a combination of stylometric features extracted from emails, and social features extracted from an online social network to detect targeted spear phishing emails.
