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Puneet Mathur

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5 papers
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5

AAAI Conference 2023 Conference Paper

DocEdit: Language-Guided Document Editing

  • Puneet Mathur
  • Rajiv Jain
  • Jiuxiang Gu
  • Franck Dernoncourt
  • Dinesh Manocha
  • Vlad I. Morariu

Professional document editing tools require a certain level of expertise to perform complex edit operations. To make editing tools accessible to increasingly novice users, we investigate intelligent document assistant systems that can make or suggest edits based on a user's natural language request. Such a system should be able to understand the user's ambiguous requests and contextualize them to the visual cues and textual content found in a document image to edit localized unstructured text and structured layouts. To this end, we propose a new task of language-guided localized document editing, where the user provides a document and an open vocabulary editing request, and the intelligent system produces a command that can be used to automate edits in real-world document editing software. In support of this task, we curate the DocEdit dataset, a collection of approximately 28K instances of user edit requests over PDF and design templates along with their corresponding ground truth software executable commands. To our knowledge, this is the first dataset that provides a diverse mix of edit operations with direct and indirect references to the embedded text and visual objects such as paragraphs, lists, tables, etc. We also propose DocEditor, a Transformer-based localization-aware multimodal (textual, spatial, and visual) model that performs the new task. The model attends to both document objects and related text contents which may be referred to in a user edit request, generating a multimodal embedding that is used to predict an edit command and associated bounding box localizing it. Our proposed model empirically outperforms other baseline deep learning approaches by 15-18%, providing a strong starting point for future work.

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AAAI Conference 2020 Short Paper

An Iterative Approach for Identifying Complaint Based Tweets in Social Media Platforms (Student Abstract)

  • Gyanesh Anand
  • Akash Gautam
  • Puneet Mathur
  • Debanjan Mahata
  • Rajiv Ratn Shah
  • Ramit Sawhney

Twitter is a social media platform where users express opinions over a variety of issues. Posts offering grievances or complaints can be utilized by private/ public organizations to improve their service and promptly gauge a low-cost assessment. In this paper, we propose an iterative methodology which aims to identify complaint based posts pertaining to the transport domain. We perform comprehensive evaluations along with releasing a novel dataset for the research purposes1.

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AAAI Conference 2020 Short Paper

ESAS: Towards Practical and Explainable Short Answer Scoring (Student Abstract)

  • Palak Goenka
  • Mehak Piplani
  • Ramit Sawhney
  • Puneet Mathur
  • Rajiv Ratn Shah

Motivated by the mandate to design and deploy a practical, real-world educational tool for grading, we extensively explore linguistic patterns for Short Answer Scoring (SAS) as well as authorship feedback. We approach the SAS task via a multipronged approach that employs linguistic context features for capturing domain-specific knowledge while emphasizing on domain agnostic grading and detailed feedback via an ensemble of explainable statistical models. Our methodology quantitatively supersedes multiple automatic short answer scoring systems.

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AAAI Conference 2020 Conference Paper

Hindi-English Hate Speech Detection: Author Profiling, Debiasing, and Practical Perspectives

  • Shivang Chopra
  • Ramit Sawhney
  • Puneet Mathur
  • Rajiv Ratn Shah

Code-switching in linguistically diverse, low resource languages is often semantically complex and lacks sophisticated methodologies that can be applied to real-world data for precisely detecting hate speech. In an attempt to bridge this gap, we introduce a three-tier pipeline that employs profanity modeling, deep graph embeddings, and author profiling to retrieve instances of hate speech in Hindi-English codeswitched language (Hinglish) on social media platforms like Twitter. Through extensive comparison against several baselines on two real-world datasets, we demonstrate how targeted hate embeddings combined with social network-based features outperform state of the art, both quantitatively and qualitatively. Additionally, we present an expert-in-the-loop algorithm for bias elimination in the proposed model pipeline and study the prevalence and performance impact of the debiasing. Finally, we discuss the computational, practical, ethical, and reproducibility aspects of the deployment of our pipeline across the Web.

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AAAI Conference 2020 Short Paper

Suicide Risk Assessment via Temporal Psycholinguistic Modeling (Student Abstract)

  • Puneet Mathur
  • Ramit Sawhney
  • Rajiv Ratn Shah

Social media platforms are increasingly being used for studying psycho-linguistic phenomenon to model expressions of suicidal intent in tweets. Most recent work in suicidal ideation detection doesn’t leverage contextual psychological cues. In this work, we hypothesize that the contextual information embedded in the form of historical activities of users and homophily networks formed between like-minded individuals in Twitter can substantially improve existing techniques for automated identification of suicidal tweets. This premise is extensively tested to yield state of the art results as compared to linguistic only models, and the state-of-the-art model.

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