Papers by Shivani Poddar
MuDoCo: Corpus for Multidomain Coreference Resolution and Referring Expression Generation (2020.lrec-1)
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| Challenge: | a new dataset, MuDoCo, is composed of authored dialogs between a fictional user and a system . the dialogs cross domains and users exhibit complex task switching behavior . |
| Approach: | They propose a new dataset, MuDoCo, composed of authored dialogs between a fictional user and a system . they propose two baseline models for the downstream tasks: coreference resolution and referring expression generation. |
| Outcome: | The proposed dataset contains 8,429 dialogs with an average of 5.36 turns per dialog . the users exhibit complex task switching behavior such as re-initiating a previous task . |
Attentive Interaction Model: Modeling Changes in View in Argumentation (N18-1)
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| Challenge: | Prior work on argumentation in the NLP community has focused mainly on the first goal and has missed more nuanced and complex details of viewpoints. |
| Approach: | They propose a neural architecture that explicitly models the interplay between an Opinion Holder's (OH's) reasoning and a challenger's argument to predict if the argument succeeded in altering the OH' s view. |
| Outcome: | The proposed model outperforms several baselines on discussions on the Change My View forum on Reddit. |
Situated and Interactive Multimodal Conversations (2020.coling-main)
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Seungwhan Moon, Satwik Kottur, Paul Crook, Ankita De, Shivani Poddar, Theodore Levin, David Whitney, Daniel Difranco, Ahmad Beirami, Eunjoon Cho, Rajen Subba, Alborz Geramifard
| Challenge: | Situated Interactive MultiModal Conversations (SIMMC) is a new direction for virtual assistants that handle multimodal inputs and perform multimodal actions. |
| Approach: | They propose to use Situated Interactive MultiModal Conversations (SIMMC) to train agents to take multimodal actions grounded in a co-evolving multimodal context. |
| Outcome: | The proposed model will be made publicly available. |