Papers by Harshit Joshi
A Practical Dialogue-Act-Driven Conversation Model for Multi-Turn Response Selection (D19-1)
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| Challenge: | Dialogue acts are important in conversation modeling, but they are rarely available for new conversations. |
| Approach: | They propose an end-to-end multi-task model that integrates dialogue acts with context and response in a crossway fashion. |
| Outcome: | The proposed model improves the accuracy of the dialogue act prediction task and the MRR for the response selection task. |
Controllable and Reliable Knowledge-Intensive Task-Oriented Conversational Agents with Declarative Genie Worksheets (2025.acl-long)
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| Challenge: | Existing LLMs suffer from hallucination, following instructions with conditional logic, and integrating knowledge from different sources. |
| Approach: | They propose a programmable framework for creating knowledge-intensive task-oriented conversational agents that handle involved interactions and answer complex queries. |
| Outcome: | The proposed framework outperforms SOTA methods on complex logic dialogue datasets by up to 20.5%. |
Neural Conversational QA: Learning to Reason vs Exploiting Patterns (2020.emnlp-main)
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| Challenge: | Neural Conversational QA tasks such as ShARC require systems to answer questions based on the contents of a given passage. |
| Approach: | They propose to modify a data-set with fewer spurious patterns to exploit them . they also propose to build a heuristic-based program to exploit spurious clues . |
| Outcome: | The proposed program exploits spurious patterns in the ShARC dataset, compared to neural models. |
A Time-Aware Transformer Based Model for Suicide Ideation Detection on Social Media (2020.emnlp-main)
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| Challenge: | Suicide ideation is often linked to a history of mental depression. |
| Approach: | They propose a time-aware transformer based model for preliminary screening of suicidal risk on social media that augments linguistic models with historical context. |
| Outcome: | The proposed model outperforms competing models and shows that it is time-aware and contextually useful for suicide risk assessment. |
Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning (2021.naacl-main)
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| Challenge: | Recent studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners. |
| Approach: | They propose a framework leveraging a user’s emotional history and social information from a users neighborhood in a network to contextualize the interpretation of the latest tweet of a Twitter user. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on this task, showing the benefits of both socially and personally contextualized representations. |
PHASE: Learning Emotional Phase-aware Representations for Suicide Ideation Detection on Social Media (2021.eacl-main)
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| Challenge: | Recent studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners. |
| Approach: | They propose a time-and-phase-aware framework that adaptively learns features from a user’s historical emotional spectrum to contextualize suicidal intent. |
| Outcome: | The proposed framework outperforms state-of-the-art methods while outperforming existing methods. |
Dialogue-act-driven Conversation Model : An Experimental Study (C18-1)
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| Challenge: | In the last decade, natural language processing and machine learning have come a long way towards building an automated dialogue system. |
| Approach: | They propose a way to encode dialogue act information and use it to build a model that can use it in a natural way. |
| Outcome: | The proposed model outperforms baseline models on a new daily dialogue dataset and achieves an MRR of about 84.8%. |