Papers by Niyati Chhaya
Counterfactuals to Control Latent Disentangled Text Representations for Style Transfer (2021.acl-short)
Copied to clipboard
| Challenge: | Existing methods for unsupervised text style transfer focus on transferring a specific attribute, but this technique has never been explored in natural language generation tasks. |
| Approach: | They propose a counterfactual-based method to modify latent representations by posing a ‘what-if’ scenario. |
| Outcome: | The proposed method is tested on multiple attribute transfer tasks like Sentiment, Formality and Excitement to support the hypothesis. |
AUTOSUMM: Automatic Model Creation for Text Summarization (2021.emnlp-main)
Copied to clipboard
Sharmila Reddy Nangi, Atharv Tyagi, Jay Mundra, Sagnik Mukherjee, Raj Snehal, Niyati Chhaya, Aparna Garimella
| Challenge: | Recent efforts to develop deep learning models for text generation tasks are challenging for non-experts. |
| Approach: | They propose methods to automatically create deep learning models for extractive and abstractive summarization tasks using large language models. |
| Outcome: | The proposed methods achieve near state-of-the-art performance on a range of datasets. |
Diachronic degradation of language models: Insights from social media (P18-2)
Copied to clipboard
| Challenge: | Existing studies have explored whether and how language models degrade over time, i.e. why they fail to work on contemporary language. |
| Approach: | They investigate the accuracy of pre-trained language models for downstream tasks in machine learning and user profiling. |
| Outcome: | The results show that it is possible to measure diachronic drifts within social media and within the span of a few years. |
IndicIRSuite: Multilingual Dataset and Neural Information Models for Indian Languages (2024.acl-short)
Copied to clipboard
| Challenge: | IndicIRSuite is the first attempt at building large-scale Neural Information Retrieval resources for a large number of Indian languages. |
| Approach: | They introduce Neural Information Retrieval resources for 11 widely spoken Indian Languages from two major Indian language families. |
| Outcome: | Experiments show that Indic-ColBERT improves on INDIC-MARCO datasets for 11 languages, and that it can be used to improve IR for Indian languages. |
Offer a Different Perspective: Modeling the Belief Alignment of Arguments in Multi-party Debates (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing work on persuasion in online forums focuses on identifying debate winners and winning negotiation games. |
| Approach: | They adopt a hierarchical generative Variational Autoencoder model to model winning arguments . they propose competing hypotheses about the nature of argumentation . |
| Outcome: | The proposed model predicts winning arguments in reddit debates . it uses a hierarchical generative Variational Autoencoder to model argumentation . |
Leveraging Mental Health Forums for User-level Depression Detection on Social Media (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing methods to detect depression on social media platforms are limited due to the vastness of social media content and the lack of linguistic features. |
| Approach: | They propose to optimize the performance of user-level depression classification to lessen the burden on computational resources. |
| Outcome: | The proposed system outperforms baselines across standard metrics for the task of depression detection in text. |
“Let’s not Quote out of Context”: Unified Vision-Language Pretraining for Context Assisted Image Captioning (2023.acl-industry)
Copied to clipboard
| Challenge: | Large enterprises have several teams to create their content for the purpose of marketing, campaigning, or even maintaining a brand presence. |
| Approach: | They propose a new unified Vision-Language (VL) model with a focus on context-assisted image captioning where the caption is generated based on both the image and its context. |
| Outcome: | The proposed model achieves state-of-the-art with an improvement of up to 8.34 CIDEr score on the benchmark news image captioning datasets. |
Multi-label Categorization of Accounts of Sexism using a Neural Framework (D19-1)
Copied to clipboard
Pulkit Parikh, Harika Abburi, Pinkesh Badjatiya, Radhika Krishnan, Niyati Chhaya, Manish Gupta, Vasudeva Varma
| Challenge: | Sexism manifests in blatant as well as subtle ways, authors say . existing work on sexism classification has limitations in terms of categories used . authors: categorization of accounts of sexist behavior can aid in countering sextism . |
| Approach: | They propose a neural solution that can combine sentence representations with distributional and linguistic word embeddings. |
| Outcome: | a new method outperforms deep learning and traditional methods by an appreciable margin . the proposed method outpersforms several deep learning as well as traditional baselines by an approval margin compared to baselines . |
WikiTalkEdit: A Dataset for modeling Editors’ behaviors on Wikipedia (2021.naacl-main)
Copied to clipboard
| Challenge: | Using the WikiTalkEdit dataset, we show how positive emotion and the use of first-person pronouns predict a positive emotional change in a Wikipedia contributor. |
| Approach: | They introduce and analyze WikiTalkEdit, a dataset of conversations and edit histories from Wikipedia, for research in online cooperation and conversation modeling. |
| Outcome: | The proposed dataset supports the classic understanding of style matching, where positive emotion and the use of first-person pronouns predict a positive emotional change in a Wikipedia contributor. |
Open-World Factually Consistent Question Generation (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for question generation suffer from factual inconsistencies and incorrect entities and are not answerable from the input paragraph. |
| Approach: | They propose a data processing technique based on de-lexicalization for consistent question generation across domains and a model that is generic across question-generation models. |
| Outcome: | The proposed method produces entity-level factually consistent questions without significant impact on traditional metrics. |
EmpathBERT: A BERT-based Framework for Demographic-aware Empathy Prediction (2021.eacl-main)
Copied to clipboard
| Challenge: | EmpathBERT is a demographic-aware framework for empathy prediction based on BERT. |
| Approach: | They propose a demographic-aware framework for empathy prediction based on BERT and utilize user demographics to analyze user responses to stimulative news articles. |
| Outcome: | The proposed framework surpasses machine learning and deep learning models and highlights the importance of demographic information in the responses. |
Aff2Vec: Affect–Enriched Distributional Word Representations (C18-1)
Copied to clipboard
| Challenge: | Affective word distributions are not well understood in literature. |
| Approach: | They propose a model that embeds affective word interpretations into enriched word embeddings. |
| Outcome: | The proposed model outperforms the state-of-the-art in word-similarity tasks and in emotion analysis, personality detection, and frustration prediction tasks. |
He is very intelligent, she is very beautiful? On Mitigating Social Biases in Language Modelling and Generation (2021.findings-acl)
Copied to clipboard
Aparna Garimella, Akhash Amarnath, Kiran Kumar, Akash Pramod Yalla, Anandhavelu N, Niyati Chhaya, Balaji Vasan Srinivasan
| Challenge: | Existing studies have focused on mitigating social biases in context-free representations, with recent shift to contextual ones. |
| Approach: | They propose an approach to mitigate social biases in a large pre-trained contextual language model . they propose lexical co-occurrence-based bias penalization in the decoder units . |
| Outcome: | The proposed approach reduces biases in fill-in-the-blank sentences and summarizes . it also reduces the biased representations in the frameworks, the authors show . |
A Neural CRF-based Hierarchical Approach for Linear Text Segmentation (2023.findings-eacl)
Copied to clipboard
Inderjeet Nair, Aparna Garimella, Balaji Vasan Srinivasan, Natwar Modani, Niyati Chhaya, Srikrishna Karanam, Sumit Shekhar
| Challenge: | Existing methods to segment unformatted text and transcripts explicitly train to predict segment boundaries, but they fail to provide a large annotated dataset. |
| Approach: | They propose a method to generate hierarchical segmentation structures based on Wikipedia annotations by using a neural conditional random field. |
| Outcome: | The proposed method outperforms or achieves competitive performance when compared to previous state-of-the-art algorithms. |
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation (D19-1)
Copied to clipboard
| Challenge: | Emotion recognition in conversation (ERC) has received much attention lately due to its potential widespread applications in diverse areas, such as health-care, education, and human resources. |
| Approach: | They propose a graph neural network-based approach to emotion recognition in conversation that leverages self and inter-speaker dependency of the interlocutors to model conversational context. |
| Outcome: | The proposed method outperforms the current state-of-the-art on a number of benchmark emotion classification datasets while minimizing context propagation issues. |
Semi-supervised Multi-task Learning for Multi-label Fine-grained Sexism Classification (2020.coling-main)
Copied to clipboard
| Challenge: | Sexism is a form of oppression based on one's sex and is reported online in numerous ways. |
| Approach: | They propose a multi-task approach for fine-grained multi-label sexism classification that leverages several supporting tasks without incurring manual labeling cost. |
| Outcome: | The proposed method outperforms the state-of-the-art for multi-label sexism classification on a recently released dataset across five standard metrics. |
CaM-Gen: Causally Aware Metric-Guided Text Generation (2022.findings-acl)
Copied to clipboard
| Challenge: | Content is created for a well-defined purpose, often described by a metric or signal . external metrics and content tend to have inherent relationships and not all of them may be of consequence. |
| Approach: | They propose a mechanism to guide generative models by user-defined target metrics . authors propose generative networks guided by causally significant aspects of text . |
| Outcome: | The proposed models beat baselines in terms of the target metric control while maintaining fluency and language quality of the generated text. |