Papers with Task-Oriented Dialogue
Control-DAG: Constrained Decoding for Non-Autoregressive Directed Acyclic T5 using Weighted Finite State Automata (2024.naacl-short)
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| Challenge: | Existing non-autoregressive (NAR) models fail to generate specified entity names in up to 40% of responses and produce OOV errors. |
| Approach: | They propose a constrained decoding algorithm for Directed Acyclic T5 model which offers lexical, vocabulary and length control. |
| Outcome: | The proposed model significantly improves on Schema Guided Dialogue and DART datasets, establishing strong results for Task-Oriented Dialog and Data-to-Text NLG. |
Is MultiWOZ a Solved Task? An Interactive TOD Evaluation Framework with User Simulator (2022.findings-emnlp)
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| Challenge: | Task-oriented dialogue systems are drawing more attention in recent studies . current evaluation methods use annotated utterances in multi-turn dialogue sessions . |
| Approach: | They propose an interactive evaluation framework for task-oriented dialogue (TOD) they build a goal-oriented user simulator based on pre-trained models and use it to generate dialogues . |
| Outcome: | The proposed evaluation framework achieves 98% inform and success rates in the multi-woz dataset and the proposed scores measure the response quality besides the inform and successful rates. |
I know you are different! Towards Persona Driven Knowledge-infused Dialogue Assistant (2026.eacl-long)
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| Challenge: | 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. |
| Approach: | They propose a Hinglish multimodal, multidomain, persona-based TOD dataset that captures user-agent interactions across text and visual modalities. |
| Outcome: | The proposed framework outperforms standard and ablated models in Hinglish and Hinglanish. |
SMART: Scalable Multilingual Approach for a Robust TOD System (2025.emnlp-industry)
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| Challenge: | Existing TOD frameworks face significant challenges in handling unstructured information, providing multilingual support, and engaging proactively. |
| Approach: | They propose a novel TOD framework that combines traditional pipeline elements with modern agent-based approaches and features a simplified dialogue state, intelligent clarification mechanisms, and a unified natural language generation component that eliminates response redundancy. |
| Outcome: | The proposed framework outperforms baseline systems across key metrics and integrates in an e-commerce store. |
TOD-Flow: Modeling the Structure of Task-Oriented Dialogues (2023.emnlp-main)
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Sungryull Sohn, Yiwei Lyu, Anthony Liu, Lajanugen Logeswaran, Dong-Ki Kim, Dongsub Shim, Honglak Lee
| Challenge: | Recent advances in task-oriented dialogue systems have limitations regarding transparency and controllability. |
| Approach: | They propose to infer the TOD-flow graph from dialog data annotated with dialog acts and integrate it with any dialogue model to improve its prediction performance, transparency, and controllability. |
| Outcome: | The proposed approach improves dialog act classification and response generation performance in the MultiWOZ and SGD benchmarks. |
PicPersona-TOD : A Dataset for Personalizing Utterance Style in Task-Oriented Dialogue with Image Persona (2025.naacl-long)
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| Challenge: | Existing systems produce generic, monotonic responses that lack individuality and fail to adapt to users’ personal attributes. |
| Approach: | They propose a dataset that incorporates user images as part of the persona, enabling personalized responses tailored to user-specific factors such as age or emotional context. |
| Outcome: | The proposed dataset enhances user experience, with personalized responses contributing to a more engaging interaction. |
Task-Optimized Adapters for an End-to-End Task-Oriented Dialogue System (2023.findings-acl)
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| Challenge: | Recent work on end-to-end dialogue models with pre-trained dialogue corpora shows promising performance in the conversational system. |
| Approach: | They propose an end-to-end TOD system with task-optimized adapters which learn independently per task adding only small number of parameters after fixed layers of pre-trained network. |
| Outcome: | The proposed system achieves state-of-the-art performance on the MultiWOZ benchmark compared to existing models. |
CAUSE: Counterfactual Assessment of User Satisfaction Estimation in Task-Oriented Dialogue Systems (2024.findings-acl)
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| Challenge: | a number of studies have evaluated user satisfaction estimation in TOD systems . current benchmarks for user satisfaction estimates are highly skewed towards dialogues for which the user is satisfied. |
| Approach: | They leverage large language models to generate satisfaction-aware counterfactual dialogues to augment original dialogues of a test collection. |
| Outcome: | The proposed models show higher robustness to increase in dissatisfaction labels than fine-tuned models. |
LANID: LLM-assisted New Intent Discovery (2024.lrec-main)
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| Challenge: | Data annotation is expensive in Task-Oriented Dialogue systems. |
| Approach: | They propose a framework that leverages Large Language Models' zero-shot capability to enhance the performance of a smaller text encoder on the NID task. |
| Outcome: | The proposed framework surpasses all strong baselines in both unsupervised and semi-supervised settings. |