Papers by Rishabh Joshi
ResPer: Computationally Modelling Resisting Strategies in Persuasive Conversations (2021.eacl-main)
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Ritam Dutt, Sayan Sinha, Rishabh Joshi, Surya Shekhar Chakraborty, Meredith Riggs, Xinru Yan, Haogang Bao, Carolyn Rose
| Challenge: | Existing research has failed to account for resisting strategies employed to foil persuasion attempts. |
| Approach: | They propose a framework for identifying resisting strategies in persuasive conversations . they instantiate a dataset comprising persuasion and negotiation conversations based on a hierarchical sequence-labelling neural architecture . |
| Outcome: | The proposed framework is based on two persuasive conversation datasets and leverages a hierarchical sequence-labelling neural architecture to infer resisting strategies automatically. |
LiPO: Listwise Preference Optimization through Learning-to-Rank (2025.naacl-long)
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Tianqi Liu, Zhen Qin, Junru Wu, Jiaming Shen, Misha Khalman, Rishabh Joshi, Yao Zhao, Mohammad Saleh, Simon Baumgartner, Jialu Liu, Peter J Liu, Xuanhui Wang
| Challenge: | Recent work on language models with curated feedback provides promising alternatives to RLHF . multiple responses can be ranked by reward models or AI feedback, but there is no study on directly fitting upon a list of responses. |
| Approach: | They propose a method that aligns language models with curated human feedback . they propose SLiC and DPO as promising alternatives to traditional RLHF . |
| Outcome: | The proposed method outperforms DPO and SLiC on several preference alignment tasks with curated and real rankwise preference data. |
Unsupervised Keyphrase Extraction via Interpretable Neural Networks (2023.findings-eacl)
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Rishabh Joshi, Vidhisha Balachandran, Emily Saldanha, Maria Glenski, Svitlana Volkova, Yulia Tsvetkov
| Challenge: | Prior approaches for unsupervised keyphrase extraction relied on heuristic notions of phrase importance via embedding clustering or graph centrality. |
| Approach: | They propose an approach which defines keyphrases as document phrases that are salient for predicting the topic of the document. |
| Outcome: | The proposed method alleviates the need for ad-hoc heuristics and achieves state-of-the-art results in scientific publications and news articles. |
AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue (2020.lrec-1)
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| Challenge: | Current architectures only take care of semantic and contextual information for a given query and fail to fully account for syntactic and external knowledge which are crucial for generating responses in a chit-chat system. |
| Approach: | They propose a multi-stream deep learning architecture that learns unified embeddings for query-response pairs by incorporating Graph Convolution Networks over their dependency parse. |
| Outcome: | The proposed architecture improves on the next sentence prediction task and significantly improves existing techniques. |
Keeping Up Appearances: Computational Modeling of Face Acts in Persuasion Oriented Discussions (2020.emnlp-main)
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| Challenge: | Politeness principles play a central role in shaping human interaction. |
| Approach: | They propose a generalized framework for modeling face acts in persuasion conversations using an annotated corpus and computational models. |
| Outcome: | The proposed framework reveals differences in face act utilization between asymmetric roles in persuasion conversations and predicts key conversational outcome. |
RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information (D18-1)
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| Challenge: | Distantly-supervised Relation Extraction (RE) methods ignore readily available side information. |
| Approach: | They propose a distantly-supervised neural relation extraction method which uses additional side information from KBs to train an extractor. |
| Outcome: | The proposed method improves performance even when limited side information is available. |