| Challenge: | Existing frameworks that only provide information about user preferences can be inaccurate in e-commerce recommender systems. |
| Approach: | They propose a framework which integrates the recommender system and dialog generation system by introducing information about users’ preferences. |
| Outcome: | The proposed framework can achieve better performance in both dialog generation and recommendation compared with baselines. |
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| Challenge: | Existing frameworks for multi-subgoal dialogs require a system to build a social bond with users to gain trust and develop affinity. |
| Approach: | They propose a framework for common knowledge-based multi-subgoal dialogs that divides up conversations with multiple subgoals and propose mechanisms to filter noisy knowledge and to include cleaned knowledge in the dialog response generation process. |
| Outcome: | The proposed framework obtains state-of-the-art results on a DuRecDial dataset in both automatic and human evaluation. |
Learning Neural Templates for Recommender Dialogue System (2021.emnlp-main)
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Zujie Liang, Huang Hu, Can Xu, Jian Miao, Yingying He, Yining Chen, Xiubo Geng, Fan Liang, Daxin Jiang
| Challenge: | Recent advances in neural models have shown promising progress on this task, but key challenges remain . |
| Approach: | They propose a framework that can decouple dialogue generation from item recommendation . they use a response template generator and item selector to generate a responses template . |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on the benchmark ReDial. |
RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models (2022.aacl-main)
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| Challenge: | Existing generative methods to recommend items are shallowly integrated into the model training and have poor chit-chat ability. |
| Approach: | They propose a framework that integrates recommendation into the dialog generation by introducing a vocabulary pointer. |
| Outcome: | The proposed framework outperforms the state-of-the-art models on a benchmark dataset. |
INSPIRED: Toward Sociable Recommendation Dialog Systems (2020.emnlp-main)
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| Challenge: | Existing studies on recommendation dialog systems lack a study on communication strategies used by human speakers for making successful and persuasive recommendations. |
| Approach: | They propose to annotate a dataset of human-human movie recommendation dialogs with sociable recommendation strategies. |
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External Knowledge Acquisition for End-to-End Document-Oriented Dialog Systems (2023.eacl-main)
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| Challenge: | End-to-end neural models for conversational AI often assume that a response can be generated by considering only the knowledge acquired during training. |
| Approach: | They propose an architecture for document-oriented conversations with access to external knowledge sources. |
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CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendation (2021.emnlp-main)
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| Challenge: | Existing systems that explore user preference through conversational interactions do not exploit the context and knowledge to make accurate recommendations. |
| Approach: | They propose a model that performs tree-structured reasoning on a knowledge graph and generates informative dialog acts to guide language generation. |
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Multi-Grained Knowledge Retrieval for End-to-End Task-Oriented Dialog (2023.acl-long)
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| Challenge: | Existing systems blend knowledge retrieval with response generation and optimize them with direct supervision from reference responses. |
| Approach: | They propose a multi-grained knowledge retrieval system that decouples knowledge retrievals from response generation and introduces an entity selector and an attribute selector to acquire multigrained information from the knowledge base. |
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Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever (D19-1)
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| Challenge: | Existing work on sequence-to-sequence dialogues treats the KB query as an attention over the entire KB without the guarantee that the generated entities are consistent with each other. |
| Approach: | They propose a framework which queries the knowledge base in two steps to improve consistency . they first return the most relevant KB row given a dialogue history . |
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Learning When and What to Quote: A Quotation Recommender System with Mutual Promotion of Recommendation and Generation (2022.findings-emnlp)
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| Challenge: | Existing quotation recommendation system focuses on what to quote, but ignores whether or when to quote. |
| Approach: | They propose a framework that learns to predict when to quote and what to quote jointly. |
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Unsupervised Learning of KB Queries in Task-Oriented Dialogs (2021.tacl-1)
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| Challenge: | Existing approaches require dialog datasets to explicitly annotate knowledge base (KB) queries. |
| Approach: | They propose a pipelined approach to predict when to make a KB query and train the dialog agent without explicit annotation. |
| Outcome: | The proposed approach predicts when to make a KB query, then predicts a query at the predicted position and uses the results in subsequent dialog. |