Search Results for author:
Found 77 papers, 21 papers with code
Leveraging Mental Health Forums for User-level Depression Detection on Social Media
We leverage accounts of depression taken from this dataset to infuse domain-specific elements into our framework.
SciBERT Sentence Representation for Citation Context Classification
We participated in both subtask A and subtask B.
Cross-lingual Alignment of Knowledge Graph Triples with Sentences
We propose cross-lingual pairing of English triples with Hindi sentences to mitigate the unavailability of content overlap.
IIIT-MLNS at SemEval-2022 Task 8: Siamese Architecture for Modeling Multilingual News Similarity
For modeling the similarity task by using the representations given by these models, a Siamese architecture was used as the underlying architecture.
IIITH at SemEval-2022 Task 5: A comparative study of deep learning models for identifying misogynous memes
This paper provides a comparison of different deep learning methods for identifying misogynous memes for SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification.
An Ensemble Approach to Detect Emotions at an Essay Level
This paper describes our system (IREL, reffered as himanshu. 1007 on Codalab) for Shared Task on Empathy Detection, Emotion Classification, and Personality Detection at 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis at ACL 2022.
Towards Capturing Changes in Mood and Identifying Suicidality Risk
This paper describes our systems for CLPsych? s 2022 Shared Task.
MetaCheckGPT -- A Multi-task Hallucination Detector Using LLM Uncertainty and Meta-models
Hallucinations in large language models (LLMs) have recently become a significant problem.
Can LLMs Generate Architectural Design Decisions? -An Exploratory Empirical study
In our exploratory study, we utilize GPT and T5-based models with 0-shot, few-shot, and fine-tuning approaches to generate the Decision of an ADR given its Context.
Multilingual Bias Detection and Mitigation for Indian Languages
Next, we investigate the effectiveness of popular multilingual Transformer-based models for the two tasks by modeling detection as a binary classification problem and mitigation as a style transfer problem.
Neural models for Factual Inconsistency Classification with Explanations
The proposed system first predicts inconsistent spans from claim and context; and then uses them to predict inconsistency types and inconsistent entity types (when inconsistency is due to entities).
LLM-RM at SemEval-2023 Task 2: Multilingual Complex NER using XLM-RoBERTa
Named Entity Recognition(NER) is a task of recognizing entities at a token level in a sentence.
Summarizing Indian Languages using Multilingual Transformers based Models
With the advent of multilingual models like mBART, mT5, IndicBART etc., summarization in low resource Indian languages is getting a lot of attention now a days.
GrapeQA: GRaph Augmentation and Pruning to Enhance Question-Answering
Commonsense question-answering (QA) methods combine the power of pre-trained Language Models (LM) with the reasoning provided by Knowledge Graphs (KG).
Ranked #8 on
Question Answering
on OpenBookQA
XWikiGen: Cross-lingual Summarization for Encyclopedic Text Generation in Low Resource Languages
But, for low-resource languages, the scarcity of reference articles makes monolingual summarization ineffective in solving this problem.
Ranked #1 on
Cross-Lingual Abstractive Summarization
on XWikiRef
Cross-Lingual Abstractive Summarization
Document Summarization
+3
Massively Multilingual Language Models for Cross Lingual Fact Extraction from Low Resource Indian Languages
Cross Lingual Information Extraction aims at extracting factual information in the form of English triples from low resource Indian Language text.
Investigating Strategies for Clause Recommendation
Clause recommendation is the problem of recommending a clause to a legal contract, given the context of the contract in question and the clause type to which the clause should belong.
Graph-based Keyword Planning for Legal Clause Generation from Topics
Generating domain-specific content such as legal clauses based on minimal user-provided information can be of significant benefit in automating legal contract generation.
Towards Proactively Forecasting Sentence-Specific Information Popularity within Online News Documents
We model sentence-specific popularity forecasting as a sequence regression task.
Gui at MixMT 2022 : English-Hinglish: An MT approach for translation of code mixed data
The first task dealt with both Roman and Devanagari script as we had monolingual data in both English and Hindi whereas the second task only had data in Roman script.
XF2T: Cross-lingual Fact-to-Text Generation for Low-Resource Languages
Our extensive experiments show that a multi-lingual mT5 model which uses fact-aware embeddings with structure-aware input encoding leads to best results on average across the twelve languages.
Ranked #1 on
Data-to-Text Generation
on XAlign
XAlign: Cross-lingual Fact-to-Text Alignment and Generation for Low-Resource Languages
Multiple critical scenarios (like Wikipedia text generation given English Infoboxes) need automated generation of descriptive text in low resource (LR) languages from English fact triples.
Ranked #3 on
Data-to-Text Generation
on XAlign
Transformer Models for Text Coherence Assessment
Coherence is an important aspect of text quality and is crucial for ensuring its readability.
Ranked #1 on
Coherence Evaluation
on GCDC + RST - Accuracy
IIITH at SemEval-2021 Task 7: Leveraging transformer-based humourous and offensive text detection architectures using lexical and hurtlex features and task adaptive pretraining
This paper describes our approach (IIITH) for SemEval-2021 Task 5: HaHackathon: Detecting and Rating Humor and Offense.
Knowledge-based Extraction of Cause-Effect Relations from Biomedical Text
We propose a knowledge-based approach for extraction of Cause-Effect (CE) relations from biomedical text.
Identifying COVID-19 Fake News in Social Media
The evolution of social media platforms have empowered everyone to access information easily.
Summaformers @ LaySumm 20, LongSumm 20
Traditionally, various feature engineering and machine learning based systems have been proposed for extractive as well as abstractive text summarization.
Ranked #2 on
Extended Summarization
on Longsumm Blind Test
Task Adaptive Pretraining of Transformers for Hostility Detection
Identifying adverse and hostile content on the web and more particularly, on social media, has become a problem of paramount interest in recent years.
Leveraging Multilingual Transformers for Hate Speech Detection
Our work leverages state of the art Transformer language models to identify hate speech in a multilingual setting.
SIS@IIITH at SemEval-2020 Task 8: An Overview of Simple Text Classification Methods for Meme Analysis
We chose to participate only in Task A which dealt with Sentiment Classification, which we formulated as a text classification problem.
Semi-supervised Multi-task Learning for Multi-label Fine-grained Sexism Classification
Sexism, a form of oppression based on one{'}s sex, manifests itself in numerous ways and causes enormous suffering.
Predicting Clickbait Strength in Online Social Media
It is not only essential to identify a click-bait, but also to identify the intensity of the clickbait based on the strength of the clickbait.
Extracting Message Sequence Charts from Hindi Narrative Text
In this paper, we propose the use of Message Sequence Charts (MSC) as a representation for visualizing narrative text in Hindi.
Stereotypical Bias Removal for Hate Speech Detection Task using Knowledge-based Generalizations
Knowledge-based generalization provides an effective way to encode knowledge because the abstraction they provide not only generalizes content but also facilitates retraction of information from the hate speech detection classifier, thereby reducing the imbalance.
Unity in Diversity: Learning Distributed Heterogeneous Sentence Representation for Extractive Summarization
While the conventional approaches rely on human crafted document-independent features to generate a summary, we develop a data-driven novel summary system called HNet, which exploits the various semantic and compositional aspects latent in a sentence to capture document independent features.
Hybrid MemNet for Extractive Summarization
Extractive text summarization has been an extensive research problem in the field of natural language understanding.
Multi-label Categorization of Accounts of Sexism using a Neural Framework
Sexism, an injustice that subjects women and girls to enormous suffering, manifests in blatant as well as subtle ways.
Adapting Language Models for Non-Parallel Author-Stylized Rewriting
Given the recent progress in language modeling using Transformer-based neural models and an active interest in generating stylized text, we present an approach to leverage the generalization capabilities of a language model to rewrite an input text in a target author's style.
Extraction of Message Sequence Charts from Narrative History Text
In this paper, we advocate the use of Message Sequence Chart (MSC) as a knowledge representation to capture and visualize multi-actor interactions and their temporal ordering.
Extraction of Message Sequence Charts from Software Use-Case Descriptions
We apply this tool to extract MSCs from several real-life software use-case descriptions and show that it performs better than the existing techniques.
Fermi at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media using Sentence Embeddings
This paper describes our system (Fermi) for Task 6: OffensEval: Identifying and Categorizing Offensive Language in Social Media of SemEval-2019.
FERMI at SemEval-2019 Task 5: Using Sentence embeddings to Identify Hate Speech Against Immigrants and Women in Twitter
This paper describes our system (Fermi) for Task 5 of SemEval-2019: HatEval: Multilingual Detection of Hate Speech Against Immigrants and Women on Twitter.
Fermi at SemEval-2019 Task 8: An elementary but effective approach to Question Discernment in Community QA Forums
This information is highly useful in segregating factual questions from non-factual ones which highly helps in organizing the questions into useful categories and trims down the problem space for the next task in the pipeline for fact evaluation among the available answers.
When science journalism meets artificial intelligence : An interactive demonstration
We present an online interactive tool that generates titles of blog titles and thus take the first step toward automating science journalism.
Attention-based Neural Text Segmentation
Text segmentation plays an important role in various Natural Language Processing (NLP) tasks like summarization, context understanding, document indexing and document noise removal.
SWDE : A Sub-Word And Document Embedding Based Engine for Clickbait Detection
We generate sub-word level embeddings of the title using Convolutional Neural Networks and use them to train a bidirectional LSTM architecture.
Identification of Alias Links among Participants in Narratives
Identification of distinct and independent participants (entities of interest) in a narrative is an important task for many NLP applications.
ELDEN: Improved Entity Linking Using Densified Knowledge Graphs
Entity Linking (EL) systems aim to automatically map mentions of an entity in text to the corresponding entity in a Knowledge Graph (KG).
Ranked #3 on
Entity Linking
on CoNLL-Aida
Co-training for Extraction of Adverse Drug Reaction Mentions from Tweets
Adverse drug reactions (ADRs) are one of the leading causes of mortality in health care.
Multi-Task Learning for Extraction of Adverse Drug Reaction Mentions from Tweets
Adverse drug reactions (ADRs) are one of the leading causes of mortality in health care.
SSAS: Semantic Similarity for Abstractive Summarization
Ideally a metric evaluating an abstract system summary should represent the extent to which the system-generated summary approximates the semantic inference conceived by the reader using a human-written reference summary.
Abstractive Text Summarization
Natural Language Inference
+2
Abstractive Multi-document Summarization by Partial Tree Extraction, Recombination and Linearization
The method entrusts the summarizer to generate its own topically coherent sequential structures from scratch for effective communication.
Identifying Clickbait: A Multi-Strategy Approach Using Neural Networks
The application of neural networks for this task has only been explored partially.
Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction Mention Extraction
Current methods in ADR mention extraction relies on supervised learning methods, which suffers from labeled data scarcity problem.
Deep Learning for Hate Speech Detection in Tweets
Hate speech detection on Twitter is critical for applications like controversial event extraction, building AI chatterbots, content recommendation, and sentiment analysis.
Interpretation of Semantic Tweet Representations
Research in analysis of microblogging platforms is experiencing a renewed surge with a large number of works applying representation learning models for applications like sentiment analysis, semantic textual similarity computation, hashtag prediction, etc.
Improving Tweet Representations using Temporal and User Context
In this work we propose a novel representation learning model which computes semantic representations for tweets accurately.
Interpreting the Syntactic and Social Elements of the Tweet Representations via Elementary Property Prediction Tasks
Research in social media analysis is experiencing a recent surge with a large number of works applying representation learning models to solve high-level syntactico-semantic tasks such as sentiment analysis, semantic textual similarity computation, hashtag prediction and so on.
Towards Sub-Word Level Compositions for Sentiment Analysis of Hindi-English Code Mixed Text
We introduce a Hindi-English (Hi-En) code-mixed dataset for sentiment analysis and perform empirical analysis comparing the suitability and performance of various state-of-the-art SA methods in social media.
Towards Deep Semantic Analysis Of Hashtags
Hashtags are semantico-syntactic constructs used across various social networking and microblogging platforms to enable users to start a topic specific discussion or classify a post into a desired category.
Enrichment of Bilingual Dictionary through News Stream Data
Many time the language pair does not have large bilingual comparable corpora and in such cases the best automatic dictionary is upper bounded by the quality and coverage of such corpora.
Exploring the Role of Logically Related Non-Question Phrases for Answering Why-Questions
Next, we apply an improved version of ranking with a prior-based approach, which ranks all words in the candidate document with respect to a set of root words (i. e. non-stopwords present in the question and in the candidate document).
Hindi Subjective Lexicon: A Lexical Resource for Hindi Adjective Polarity Classification
With recent developments in web technologies, percentage web content in Hindi is growing up at a lighting speed.
