| Challenge: | Typical human-machine conversation systems only use utterances and responses as training data, which results in uninformative and inappropriate responses. |
| Approach: | They propose a dataset where one acts as a conversation leader and the other as 'follower' they establish baseline results on a 270K utterances and 30k dialogues dataset using state-of-the-art models. |
| Outcome: | The proposed model can generate diverse multi-turn conversations using knowledge from a new dataset . |
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NewsDialogues: Towards Proactive News Grounded Conversation (2023.findings-acl)
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Siheng Li, Yichun Yin, Cheng Yang, Wangjie Jiang, Yiwei Li, Zesen Cheng, Lifeng Shang, Xin Jiang, Qun Liu, Yujiu Yang
| Challenge: | Hot news is one of the most popular topics in daily conversations. |
| Approach: | They propose a task where a dialogue system can lead the conversation based on key topics of the news. |
| Outcome: | The proposed method can lead conversations based on key topics of the news . it can also be used in information-seeking and chit-chat scenarios . |
Goal Awareness for Conversational AI: Proactivity, Non-collaborativity, and Beyond (2023.acl-tutorials)
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| Challenge: | Conventional conversation researches focus on the responseability of the system, such as dialogue context understanding and response generation, but overlook the design of an essential property in intelligent conversations, i.e., goal awareness. |
| Approach: | This tutorial introduces the latest advances on the design of agent’s awareness of goals in a wide range of conversational systems. |
| Outcome: | This tutorial introduces the latest advances on the design of agent’s awareness of goals in a wide range of conversational systems. |
Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curation (2023.emnlp-main)
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| Challenge: | a recent study defines a conversation target from the system side to proactively steer conversations toward predefined targets or accomplish specific system-side goals. |
| Approach: | They propose a dataset curation framework that automatically curations a large-scale personalized dialogue dataset using a role-playing approach. |
| Outcome: | The proposed dataset is of high quality and could contribute to exploring personalized target-oriented dialogue. |
Towards a Progression-Aware Autonomous Dialogue Agent (2022.naacl-main)
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| Challenge: | Recent advances in large-scale language modeling and generation have enabled the creation of dialogue agents that exhibit human-like responses in a wide range of conversational scenarios. |
| Approach: | They propose a framework in which dialogue agents can evaluate the progression of a conversation toward or away from desired outcomes and use this signal to inform planning for subsequent responses. |
| Outcome: | The proposed framework evaluates the progression of a conversation toward or away from desired outcomes and uses this signal to inform planning for subsequent responses. |
A Textual Dataset for Situated Proactive Response Selection (2023.acl-long)
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| Challenge: | Recent data-driven conversational models can return fluent, consistent, and informative responses to many kinds of requests and utterances in task-oriented scenarios. |
| Approach: | They propose a task of proactive response selection based on situational information and a dataset of 1.7k English conversation examples that include situational background information and for each conversation a set of responses. |
| Outcome: | The proposed model can only provide fluent, consistent, and informative responses to a set of 1.7k English conversation examples and is not easy to perform for strong neural models. |
Human-like informative conversations: Better acknowledgements using conditional mutual information (2021.naacl-main)
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| Challenge: | Existing chatbots generate responses that are non-specific w.r.t. one of the contexts, typically the conversational history. |
| Approach: | They propose to build a dialogue agent that can weave new factual content into conversations as naturally as humans. |
| Outcome: | The proposed method trades off pmi for pcmi_h and is preferred by humans for overall quality over the Max-PMI baseline 60% of the time. |
Conversation Initiation by Diverse News Contents Introduction (N19-1)
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| Challenge: | Existing conversation systems assume that the user always initiates conversation and focus on how to respond to the given user’s utterance. |
| Approach: | They propose to generate initial utterance by summarizing and chatting about news articles to avoid boredom by relying on boilerplate utterrances like greetings. |
| Outcome: | The proposed model outperforms baseline models and based on information retrieval based and generation based models. |
Beyond Candidates : Adaptive Dialogue Agent Utilizing Persona and Knowledge (2023.findings-emnlp)
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| Challenge: | a previous study suggested that human dialogue systems ground persona and knowledge but they require incomplete candidate sets. |
| Approach: | They propose an adaptive dialogue agent that uses persona and knowledge without candidate sets . their model generates consistent and relevant persona descriptions and identifies relevant knowledge . |
| Outcome: | The proposed model outperforms baselines that ground persona and knowledge candidates even with fragmentary information. |
Towards Exploiting Background Knowledge for Building Conversation Systems (D18-1)
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| Challenge: | Existing dialog datasets contain a sequence of utterances without any explicit background knowledge associated with them. |
| Approach: | They propose to use movie chats to generate responses by copying unstructured background knowledge . they use a dataset of 9K conversations to test whether responses are generated by copy-and-modify models . |
| Outcome: | The proposed model mimics human process of conversing by copying and/or modifying sentences from unstructured background knowledge. |
Target-Guided Open-Domain Conversation (P19-1)
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| Challenge: | a new study aims to improve opendomain chat systems by integrating goals and strategy into the system. |
| Approach: | They propose a structured approach that introduces coarse-grained keywords to control intended content of system responses and attains smooth conversation transition through turn-level supervised learning. |
| Outcome: | The proposed system produces meaningful and effective conversations significantly better than other approaches. |