Papers with conversation systems
Agent Assist through Conversation Analysis (2020.emnlp-demos)
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Kshitij Fadnis, Nathaniel Mills, Jatin Ganhotra, Haggai Roitman, Gaurav Pandey, Doron Cohen, Yosi Mass, Shai Erera, Chulaka Gunasekara, Danish Contractor, Siva Patel, Q. Vera Liao, Sachindra Joshi, Luis Lastras, David Konopnicki
| Challenge: | Using conversational approach to information retrieval for agent assistance, customer support agents are a critical part of an organization's customer support team. |
| Approach: | They propose a conversational approach to information retrieval for agent assistance that monitors an evolving conversation and recommends both responses and URLs of documents. |
| Outcome: | The proposed system monitors an evolving conversation and recommends both responses and URLs of documents the agent can use in replies to their client. |
A Dataset for Building Code-Mixed Goal Oriented Conversation Systems (C18-1)
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| Challenge: | Existing data on goal-oriented conversation systems focus on monolingual conversations and there is hardly any work on multilingual and/or code-mixed conversations. |
| Approach: | They build a goal-oriented dialog dataset containing code-mixed conversations using monolingual text from a restaurant reservation dataset. |
| Outcome: | The proposed model is based on a restaurant reservation dataset and will be made publicly available for research purposes. |
Proactive Human-Machine Conversation with Explicit Conversation Goal (P19-1)
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| 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 . |
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. |
Empathetic and Emotionally Positive Conversation Systems with an Emotion-specific Query-Response Memory (2022.findings-emnlp)
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Zhiliang Tian, Yinliang Wang, Yiping Song, Chi Zhang, Dongkyu Lee, Yingxiu Zhao, Dongsheng Li, Nevin L. Zhang
| Challenge: | Existing emotional conversation systems output responses according to either a given emotion or the user’s emotion reflected in the input queries. |
| Approach: | They propose to generate empathetic responses catering to the user’s emotions while leading the conversation to be emotionally positive by abstracting the conversation corpus and extracting the different responding strategies for different users’ emotions and conversational topics into a memory. |
| Outcome: | The proposed model surpasses the baseline methods in appropriateness, diversity, and generating emotionally positive responses. |