Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks (D19-1)
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| Challenge: | Goal-oriented dialogue systems are now being widely adopted in industry where it is of key importance to maintain a rapid prototyping cycle for new products and domains. |
| Approach: | They propose a data-driven approach to goal-oriented dialogue generation which only uses a few example dialogues, none of which has to be annotated. |
| Outcome: | The proposed approach significantly improves upon baseline models and over the previous state-of-the-art model, ZSDG. |
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| Challenge: | NeuralWOZ generates dialogues from user’s goal instructions and system’s API call results. |
| Approach: | They propose a framework that uses model-based dialogue simulation to generate dialogues from user’s goal instructions and system’s API call results. |
| Outcome: | The proposed framework achieves 4.4% point joint goal accuracy on average across domains and 5.7% point of zero-shot coverage against the MultiWOZ 2.1 dataset. |
Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems (2021.naacl-demos)
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Anish Acharya, Suranjit Adhikari, Sanchit Agarwal, Vincent Auvray, Nehal Belgamwar, Arijit Biswas, Shubhra Chandra, Tagyoung Chung, Maryam Fazel-Zarandi, Raefer Gabriel, Shuyang Gao, Rahul Goel, Dilek Hakkani-Tur, Jan Jezabek, Abhay Jha, Jiun-Yu Kao, Prakash Krishnan, Peter Ku, Anuj Goyal, Chien-Wei Lin, Qing Liu, Arindam Mandal, Angeliki Metallinou, Vishal Naik, Yi Pan, Shachi Paul, Vittorio Perera, Abhishek Sethi, Minmin Shen, Nikko Strom, Eddie Wang
| Challenge: | Traditional goal-oriented dialogue systems require annotations which are hard to obtain for every new domain, limiting scalability. |
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Hello, It’s GPT-2 - How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems (D19-56)
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| Challenge: | Statistical conversational systems are complex, timeintensive, expensive, and not easily transferable due to data scarcity. |
| Approach: | They propose a task-oriented dialogue model that operates on text input . they validate it on multi-domain task-orientated dialogues from a multi-word dataset . |
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Domain Expert Platform for Goal-Oriented Dialog Collection (2021.eacl-demos)
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| Challenge: | a prerequisite for the creation of a goal-oriented neural network dialogue system is a dataset that represents typical dialogue scenarios and includes various semantic annotations. |
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| Outcome: | The proposed platform is language-independent and is currently being used to collect dialogue samples in Latvian . |
End-to-End Neural Pipeline for Goal-Oriented Dialogue Systems using GPT-2 (2020.acl-main)
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| Challenge: | End-to-end dialogue systems with monolithic neural architecture are often trained with input-output utterances without taking into account the entire annotations available in the corpus. |
| Approach: | They propose an end-to-end neural architecture for goal-oriented dialogue systems that addresses both challenges . they propose a modular architecture where modules are optimized individually . |
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Multi-Domain Goal-Oriented Dialogues (MultiDoGO): Strategies toward Curating and Annotating Large Scale Dialogue Data (D19-1)
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| Challenge: | a large number of goal-oriented dialogue datasets are limited in their size, linguistic diversity, domain coverage, or annotation granularity. |
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DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation (2022.findings-naacl)
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| Challenge: | Recent research focused on knowledge distillation methods where the underlying relationship between the facts in a knowledge base is not effectively captured. |
| Approach: | They propose a novel task-oriented dialogue system that effectively incorporates knowledge into a language model by using structural information of a knowledge graph. |
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Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation (C18-1)
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| Challenge: | Existing pipeline models for task-oriented dialogue system require explicit modeling of dialogue states and hand-crafted action spaces to query domain-specific knowledge base. |
| Approach: | They propose a framework that leverages the advantages of classic pipeline and sequence-to-sequence models. |
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Pan More Gold from the Sand: Refining Open-domain Dialogue Training with Noisy Self-Retrieval Generation (2022.coling-1)
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| Challenge: | Existing methods for generating open-domain dialogue systems underutilize training data. |
| Approach: | They propose a retrieval-generation training framework that takes advantage of heterogeneous training data by considering them as "evidence" they use BERTScore retrieval framework which gives better qualities of the training data, they show . |
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DiaSynth: Synthetic Dialogue Generation Framework for Low Resource Dialogue Applications (2025.findings-naacl)
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| Challenge: | Existing research is limited by general or niche datasets that lack sufficient scale for training dialogue systems. |
| Approach: | They propose a synthetic dialogue generation framework that uses Large Language Models and Chain of Thought reasoning to generate dynamic, domain-specific dialogues with simulated personas and diverse conversational features. |
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