The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents (2020.acl-main)
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| Challenge: | a set of 12 tasks that measure if a conversational agent can communicate engagingly with personality and empathy, ask questions, answer questions by utilizing knowledge resources, and perceive and converse about images. |
| Approach: | They propose a set of 12 tasks that measure if a conversational agent can communicate engagingly with personality and empathy . they use large dialogue datasets to multi-task and obtain state-of-the-art results . |
| Outcome: | The proposed model improves over a BERT pre-trained model on large dialogue datasets and provides state-of-the-art results on many of the tasks. |
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| Challenge: | Recent work in open-domain conversational agents has demonstrated that significant improvements in humanness and user preference can be achieved via massive scaling in both pre-training data and model size. |
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DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI (2024.findings-eacl)
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Jianguo Zhang, Kun Qian, Zhiwei Liu, Shelby Heinecke, Rui Meng, Ye Liu, Zhou Yu, Huan Wang, Silvio Savarese, Caiming Xiong
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Towards a Zero-Data, Controllable, Adaptive Dialog System (2024.lrec-main)
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| Challenge: | Recent approaches to controllable dialog systems require additional training data to be deployed in new domains. |
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MuDoCo: Corpus for Multidomain Coreference Resolution and Referring Expression Generation (2020.lrec-1)
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| Challenge: | a new dataset, MuDoCo, is composed of authored dialogs between a fictional user and a system . the dialogs cross domains and users exhibit complex task switching behavior . |
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Maria: A Visual Experience Powered Conversational Agent (2021.acl-long)
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| Challenge: | Existing studies focus on grounding conversational agents on text-only corpora, but they lack the perception ability to our physical world. |
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Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset (D19-1)
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Bill Byrne, Karthik Krishnamoorthi, Chinnadhurai Sankar, Arvind Neelakantan, Ben Goodrich, Daniel Duckworth, Semih Yavuz, Amit Dubey, Kyu-Young Kim, Andy Cedilnik
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Image-Chat: Engaging Grounded Conversations (2020.acl-main)
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| Challenge: | In order for machines to communicate with humans, they must understand the natural things that humans say about the world they live in and respond in kind. |
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Can You Put it All Together: Evaluating Conversational Agents’ Ability to Blend Skills (2020.acl-main)
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| Challenge: | Existing work has focused on learning specific qualities of conversational agents, but it remains unclear how to combine them. |
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HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Data (2022.findings-acl)
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| Challenge: | Existing datasets focused on multiturn dialogue systems focus on text or table information. |
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Ask No More: Deciding when to guess in referential visual dialogue (C18-1)
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| Challenge: | Using a task-oriented visual dialogue model, we add a decision-making component that decides whether to ask a follow-up question to identify a target referent in an image, or to stop the conversation to make a guess. |
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