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An Information Retrieval Approach to Short Text Conversation

arXiv 1408.6988 ↗First posted 29 Aug 2014cs.IR cs.CLcited by 18

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Abstract (arXiv)

Human computer conversation is regarded as one of the most difficult problems in artificial intelligence. In this paper, we address one of its key sub-problems, referred to as short text conversation, in which given a message from human, the computer returns a reasonable response to the message. We leverage the vast amount of short conversation data available on social media to study the issue. We propose formalizing short text conversation as a search problem at the first step, and employing state-of-the-art information retrieval (IR) techniques to carry out the task. We investigate the significance as well as the limitation of the IR approach. Our experiments demonstrate that the retrieval-based model can make the system behave rather "intelligently", when combined with a huge repository of conversation data from social media.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Aug 2014.

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Citing papers per year: 2021: 8; 2022: 6; 2023: 3; 2024: 1.

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  1. 2021RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-rankingcited by 175
  2. 2022A Survey on Retrieval-Augmented Text Generationcited by 157
  3. 2021PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrievalcited by 50
  4. 2021Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interactioncited by 21
  5. 2021Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbotscited by 14
  6. 2022Recent Advances in Neural Text Generation: A Task-Agnostic Surveycited by 14
  7. 2022Empathetic Conversational Systems: A Review of Current Advances, Gaps, and Opportunitiescited by 14
  8. 2021Content Selection Network for Document-grounded Retrieval-based Chatbotscited by 10
  9. 2023Chatbots to ChatGPT in a Cybersecurity Space: Evolution, Vulnerabilities, Attacks, Challenges, and Future Recommendationscited by 7
  10. 2021Distilling the Knowledge of Large-scale Generative Models into Retrieval Models for Efficient Open-domain Conversationcited by 4
  11. 2021Building an Efficient and Effective Retrieval-based Dialogue System via Mutual Learningcited by 3
  12. 2021Towards a Universal NLG for Dialogue Systems and Simulators with Future Bridgingcited by 2
6 more
  1. 2023Graph Ranking Contrastive Learning: A Extremely Simple yet Efficient Methodcited by 2
  2. 2022Query-Response Interactions by Multi-tasks in Semantic Search for Chatbot Candidate Retrievalcited by 1
  3. 2022Mix-and-Match: Scalable Dialog Response Retrieval using Gaussian Mixture Embeddingscited by 0
  4. 2022CrossDial: An Entertaining Dialogue Dataset of Chinese Crosstalkcited by 0
  5. 2023Re3Dial: Retrieve, Reorganize and Rescale Dialogue Corpus for Long-Turn Open-Domain Dialogue Pre-trainingcited by 0
  6. 2024A Stack-Propagation Framework for Low-Resource Personalized Dialogue Generationcited by 0