Papers by Abhinav Rastogi

6 papers
A Unified Approach to Entity-Centric Context Tracking in Social Conversations (2022.lrec-1)

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Challenge: Context Tracking is a computational task for human-human conversations . it involves identifying important entities and keeping track of their properties and relationships .
Approach: They propose to use a human-human conversation corpus for context tracking with people and location annotations to model the conversation's context.
Outcome: The proposed model is based on a large human-human conversation corpus with people and location annotations.
MoDE: Effective Multi-task Parameter Efficient Fine-Tuning with a Mixture of Dyadic Experts (2025.findings-naacl)

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Challenge: Recent efforts have explored mixtures of LoRA modules for multi-task settings, but this study reveals redundancy in the down-projection matrix of these architectures.
Approach: They propose a method to share down-projection matrix across tasks and employ atomic rank-one adapters coupled with routers that allow more sophisticated task-level specialization.
Outcome: The proposed method outperforms state-of-the-art models on a SNI benchmark and provides a practical solution for deploying lightweight models.
Show, Don’t Tell: Demonstrations Outperform Descriptions for Schema-Guided Task-Oriented Dialogue (2022.naacl-main)

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Challenge: Recent work has leveraged natural language descriptions of schema elements to enable universal dialogue systems; however, descriptions only indirectly convey schema semantics.
Approach: They propose to use schema-guided modeling to prompt seq2seq models with a labeled example dialogue to show schema semantics rather than tell them.
Outcome: The proposed model outperforms models using short examples as schema representations on two popular dialogue state tracking benchmarks.
Template Guided Text Generation for Task-Oriented Dialogue (2020.emnlp-main)

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Challenge: a new study examines the use of templates to generate natural language utterances for a large number of APIs.
Approach: They propose a schema-guided approach which conditions the generation on a natural language schema.
Outcome: The proposed method improves over strong baselines, is robust to out-of-domain inputs and shows improved sample efficiency.
Efficient Entity Embedding Construction from Type Knowledge for BERT (2022.findings-aacl)

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Challenge: Existing work has shown advantages of incorporating knowledge graphs (KGs) into BERT for various NLP tasks.
Approach: They propose to integrate knowledge graphs into BERT to train entity embeddings to include rich information of factual knowledge.
Outcome: The proposed models perform very well when combined with context.
AnyTOD: A Programmable Task-Oriented Dialog System (2023.emnlp-main)

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Challenge: a neuro-symbolic approach allows zero-shot adaptation to unseen tasks and domains . a neural LM keeps track of events that occur during a conversation and a symbolic program implements dialog policy is executed to recommend actions.
Approach: They propose an end-to-end, zero-shot task-oriented dialog system . it is designed to adapt to unseen tasks or domains without prior training .
Outcome: The proposed system can be programmed to adapt to unseen tasks without training . it reduces data collection and training requirements for enabling new TOD 1 16189 tasks .

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