@inproceedings{agrahari-etal-2026-know,
title = "{I} know you are different! Towards Persona Driven Knowledge-infused Dialogue Assistant",
author = "Agrahari, Shifali and
Mahato, Moushumi and
Tiwari, Abhisek and
Nabi, Javaid",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-long.96/",
pages = "2181--2205",
ISBN = "979-8-89176-380-7",
abstract = "Despite advances in large language models (LLMs), Task-Oriented Dialogue (TOD) systems often fall short in delivering personalized, context-rich responses, especially in low-resource, code-mixed, and multimodal settings like Hinglish (Hindi-English). To bridge this gap, we introduce \textit{HiVisTask}, the first Hinglish multimodal, multidomain, persona-based TOD dataset that captures user-agent interactions across text and visual modalities. We also propose \textit{$G^3 TOD$}, a generalizable framework that enhances personalization using three structured knowledge graphs: entity context, user persona, and commonsense reasoning, all extracted from conversation history. Extensive experiments with LLMs (e.g., LLaMA3.2, Phi3, GPT4, Mistral7b, Qwen3, Gemma3) show that \textit{$G^3 TOD$} consistently outperforms both standard and ablated baselines. We observe substantial gains across evaluation metrics (both quantitative: BLEU {\textuparrow} and qualitative: Human Eval {\textuparrow}) over existing models. The observed improvements strongly underscore the value of structured and selective contextualization in generating personalized and engaging multimodal responses."
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<abstract>Despite advances in large language models (LLMs), Task-Oriented Dialogue (TOD) systems often fall short in delivering personalized, context-rich responses, especially in low-resource, code-mixed, and multimodal settings like Hinglish (Hindi-English). To bridge this gap, we introduce HiVisTask, the first Hinglish multimodal, multidomain, persona-based TOD dataset that captures user-agent interactions across text and visual modalities. We also propose G³ TOD, a generalizable framework that enhances personalization using three structured knowledge graphs: entity context, user persona, and commonsense reasoning, all extracted from conversation history. Extensive experiments with LLMs (e.g., LLaMA3.2, Phi3, GPT4, Mistral7b, Qwen3, Gemma3) show that G³ TOD consistently outperforms both standard and ablated baselines. We observe substantial gains across evaluation metrics (both quantitative: BLEU ↑ and qualitative: Human Eval ↑) over existing models. The observed improvements strongly underscore the value of structured and selective contextualization in generating personalized and engaging multimodal responses.</abstract>
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%0 Conference Proceedings
%T I know you are different! Towards Persona Driven Knowledge-infused Dialogue Assistant
%A Agrahari, Shifali
%A Mahato, Moushumi
%A Tiwari, Abhisek
%A Nabi, Javaid
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F agrahari-etal-2026-know
%X Despite advances in large language models (LLMs), Task-Oriented Dialogue (TOD) systems often fall short in delivering personalized, context-rich responses, especially in low-resource, code-mixed, and multimodal settings like Hinglish (Hindi-English). To bridge this gap, we introduce HiVisTask, the first Hinglish multimodal, multidomain, persona-based TOD dataset that captures user-agent interactions across text and visual modalities. We also propose G³ TOD, a generalizable framework that enhances personalization using three structured knowledge graphs: entity context, user persona, and commonsense reasoning, all extracted from conversation history. Extensive experiments with LLMs (e.g., LLaMA3.2, Phi3, GPT4, Mistral7b, Qwen3, Gemma3) show that G³ TOD consistently outperforms both standard and ablated baselines. We observe substantial gains across evaluation metrics (both quantitative: BLEU ↑ and qualitative: Human Eval ↑) over existing models. The observed improvements strongly underscore the value of structured and selective contextualization in generating personalized and engaging multimodal responses.
%U https://aclanthology.org/2026.eacl-long.96/
%P 2181-2205
Markdown (Informal)
[I know you are different! Towards Persona Driven Knowledge-infused Dialogue Assistant](https://aclanthology.org/2026.eacl-long.96/) (Agrahari et al., EACL 2026)
ACL