Papers by Sandeep Mishra
Chat-Ghosting: Methods for Auto-Completion in Dialog Systems (2026.eacl-long)
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| Challenge: | Ghosting is a type-ahead completion task that predicts a user's intended input for inline query auto-completion (QAC). |
| Approach: | They propose to use ghosting to predict a user's intended input for inline query auto-completion by suggesting completions to incomplete queries. |
| Outcome: | The proposed method outperforms deep learning and deep learning methods with and without dialog context for ghosting. |
Eyes are the Windows to the Soul: Predicting the Rating of Text Quality Using Gaze Behaviour (P18-1)
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Sandeep Mathias, Diptesh Kanojia, Kevin Patel, Samarth Agrawal, Abhijit Mishra, Pushpak Bhattacharyya
| Challenge: | Existing methods to predict text quality include estimating subjective aspects of text, like structure, clarity, etc. |
| Approach: | They propose to capture gaze behaviour to help predict text quality by reporting improvements obtained by adding gaze features to traditional textual features for score prediction. |
| Outcome: | The proposed model shows that capturing gaze behaviour improves the accuracy of score prediction when the reader has fully understood the text. |
Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze Behaviour (2020.aacl-main)
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| Challenge: | Using gaze behaviour to solve automatic essay grading tasks is costly in terms of time and money. |
| Approach: | They propose to collect gaze behaviour from 48 essays and learn gaze behaviour for the rest of the essays using a multi-task learning framework. |
| Outcome: | The proposed approach achieves a statistically significant improvement over the state-of-the-art system for the essay sets where gaze data is available. |
Router-Suggest: Dynamic Routing for Multimodal Auto-Completion in Visually-Grounded Dialogs (2026.eacl-industry)
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| Challenge: | a task that grounds predictions in multimodal context is essential for chatbots, chatbot systems and healthcare consultations. |
| Approach: | They propose a task that grounds predictions in multimodal context to better capture user intent. |
| Outcome: | The proposed task can be used to predict upcoming characters in live chats using partially typed text and visual cues. |