Papers by Varun Manjunatha
Influence Functions for Sequence Tagging Models (2022.findings-emnlp)
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| Challenge: | Named Entity Recognition, Part-of-Speech tagging, and Semantic Role Labeling are standard tasks in NLP, but there has been little work on interpretability methods for sequence taging. |
| Approach: | They propose to extend influence functions to sequence tagging tasks by identifying noisy annotations in NER corpora. |
| Outcome: | The proposed methods are able to identify noisy annotations in NER corpora and are scalable. |
Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU models (2021.naacl-main)
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Mengnan Du, Varun Manjunatha, Rajiv Jain, Ruchi Deshpande, Franck Dernoncourt, Jiuxiang Gu, Tong Sun, Xia Hu
| Challenge: | Recent studies indicate that NLU models are prone to rely on shortcut features for prediction, without achieving true language understanding. |
| Approach: | They propose a shortcut mitigation framework to suppress NLU models from making overconfident predictions for samples with large shortcut degree. |
| Outcome: | The proposed framework suppresses the model from making overconfident predictions for samples with large shortcut degree. |
Keyphrase Prediction from Video Transcripts: New Dataset and Directions (2022.coling-1)
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Amir Pouran Ben Veyseh, Quan Hung Tran, Seunghyun Yoon, Varun Manjunatha, Hanieh Deilamsalehy, Rajiv Jain, Trung Bui, Walter W. Chang, Franck Dernoncourt, Thien Huu Nguyen
| Challenge: | Existing studies on keyphrase prediction have focused on formal texts and informal-text domains. |
| Approach: | They propose to annotate large-scale video transcripts with keyphrases from live-stream video . they propose to feed models with paragraph-level keyphrase extraction to foster future research . |
| Outcome: | The proposed model improves keyphrase prediction in live-stream video transcripts by feeding models with paragraph-level keyphrases. |
Learning to Color from Language (N18-2)
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| Challenge: | Automatic colorization is the process of adding color to greyscale images. |
| Approach: | They propose two different architectures for language-conditioned colorization that produce more accurate and plausible colorizations than a language-agnostic version. |
| Outcome: | The proposed architectures produce more accurate and plausible colorizations than a language-agnostic version. |
Transfer Learning and Prediction Consistency for Detecting Offensive Spans of Text (2022.findings-acl)
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| Challenge: | Existing models for toxic span detection only classify text snippets as offensive or not . a novel model seeks to simultaneously predict offensive words and opinion phrases . |
| Approach: | They propose a novel model that seeks to predict offensive words and opinion phrases simultaneously . they also introduce a regularization mechanism to encourage consistency of the model predictions . |
| Outcome: | The proposed model performs well compared to baselines on toxic span detection tasks . it predicts offensive words and opinion phrases to leverage inter-dependencies . |
Syntopical Graphs for Computational Argumentation Tasks (2021.acl-long)
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Joe Barrow, Rajiv Jain, Nedim Lipka, Franck Dernoncourt, Vlad Morariu, Varun Manjunatha, Douglas Oard, Philip Resnik, Henning Wachsmuth
| Challenge: | adler and van Doren (1940) proposed a formalized manual process for understanding a topic based on multiple viewpoints. |
| Approach: | They propose a syntopical reading process that emphasizes comparing and contrasting viewpoints to improve topic understanding. |
| Outcome: | The proposed method outperforms approaches that do not use collection-level information. |
TABBIE: Pretrained Representations of Tabular Data (2021.naacl-main)
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| Challenge: | Existing work on tabular representation-learning jointly models tables and associated text using self-supervised objective functions derived from pretrained language models such as BERT. |
| Approach: | They propose a tabular representation-learning model that integrates tabular data with a pretraining objective function that detects corrupted cells. |
| Outcome: | The proposed model understands complex table semantics and numerical trends. |
A Joint Model for Document Segmentation and Segment Labeling (2020.acl-main)
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| Challenge: | Existing approaches to text segmentation focus on document segmentation and segment labeling separately. |
| Approach: | They propose a method for jointly segmenting a document and labeling segments . they show that S-LSTM reduces segmentation error by 30% on average . |
| Outcome: | The proposed method reduces segmentation error by 30% while improving segment labeling. |
AnalystBench: Benchmarking professional long-form report generation with web-mined multimodal tasks (2026.findings-acl)
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Chau Minh Pham, Zichao Wang, Puneet Mathur, Alexa Siu, Akriti Jain, Aparna Garimella, Ananya B. Sai, Nedim Lipka, Mohit Iyyer, Varun Manjunatha
| Challenge: | Existing benchmarks decompose the end-to-end professional report generation into individual components. |
| Approach: | They propose a benchmarking tool that evaluates 20 real-world professional report generation tasks grounded in multimodal document collections. |
| Outcome: | The proposed model outperforms closed-source models on executive summarization tasks but drops significantly on long-horizon synthesis tasks. |
Decomposition-Enhanced Training for Post-Hoc Attributions in Language Models (2026.eacl-long)
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Sriram Balasubramanian, Samyadeep Basu, Koustava Goswami, Ryan A. Rossi, Varun Manjunatha, Roshan Santhosh, Ruiyi Zhang, Soheil Feizi, Nedim Lipka
| Challenge: | Existing methods for extractive QA struggle in multi-hop, abstractive, and semi-extractive settings. |
| Approach: | They propose a method that prompts models to produce answer decompositions as intermediate reasoning steps. |
| Outcome: | The proposed method outperforms existing methods and matches or exceeds state-of-the-art frontier models. |
kNN-LM Does Not Improve Open-ended Text Generation (2023.emnlp-main)
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| Challenge: | Interpolation-based retrieval-augmented language models (LMs) are a subtype of retrieval augmented language model that computes the probability of the next token by interpolating between the softmax distribution of the original LM and a token distribution formed by retrieving over an external datastore. |
| Approach: | They propose to interpolate the predicted distribution of the next word with a distribution formed from the most relevant retrievals for a given prefix. |
| Outcome: | The proposed methods do not exhibit improvements in open-ended generation quality, as measured by automatic evaluation metrics and human evaluations. |
IGA: An Intent-Guided Authoring Assistant (2021.emnlp-main)
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Simeng Sun, Wenlong Zhao, Varun Manjunatha, Rajiv Jain, Vlad Morariu, Franck Dernoncourt, Balaji Vasan Srinivasan, Mohit Iyyer
| Challenge: | Pretrained language models have improved writing assistance functions such as autocomplete, but more complex and controllable writing assistants have yet to be explored. |
| Approach: | They build an intent-guided authoring assistant that follows fine-grained author directives by specifying different writing intents. |
| Outcome: | The proposed system generates output satisfying the author's intent and can be rephrased to their liking. |
DocPilot: Copilot for Automating PDF Edit Workflows in Documents (2024.acl-demos)
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| Challenge: | Document workflow copilot system that can understand user intent and execute tasks accordingly to help users streamline their workflows. |
| Approach: | They propose an AI-assisted document workflow copilot system capable of understanding user intent and executing tasks accordingly. |
| Outcome: | The proposed system can understand user intent and execute tasks accordingly to help users streamline their workflows. |