Towards a Unified Conversational Recommendation System: Multi-task Learning via Contextualized Knowledge Distillation

• URL: http://arxiv.org/abs/2310.18119v1
• Date: Fri, 27 Oct 2023 13:06:24 GMT
• Title: Towards a Unified Conversational Recommendation System: Multi-task Learning via Contextualized Knowledge Distillation
• Authors: Yeongseo Jung, Eunseo Jung, Lei Chen
• Abstract summary: We propose a multi-task learning for a unified Conversational Recommendation System (CRS) A single model jointly learns both tasks via Contextualized Knowledge Distillation (ConKD) Our experiments demonstrate that our single model significantly improves recommendation performance while enhancing fluency, and achieves comparable results in terms of diversity.
• Score: 5.125530969984795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In Conversational Recommendation System (CRS), an agent is asked to recommend a set of items to users within natural language conversations. To address the need for both conversational capability and personalized recommendations, prior works have utilized separate recommendation and dialogue modules. However, such approach inevitably results in a discrepancy between recommendation results and generated responses. To bridge the gap, we propose a multi-task learning for a unified CRS, where a single model jointly learns both tasks via Contextualized Knowledge Distillation (ConKD). We introduce two versions of ConKD: hard gate and soft gate. The former selectively gates between two task-specific teachers, while the latter integrates knowledge from both teachers. Our gates are computed on-the-fly in a context-specific manner, facilitating flexible integration of relevant knowledge. Extensive experiments demonstrate that our single model significantly improves recommendation performance while enhancing fluency, and achieves comparable results in terms of diversity.

Related papers

• Multi-Stage Coarse-to-Fine Contrastive Learning for Conversation Intent Induction [34.25242109800481]
  This paper presents our solution to Track 2 of Intent Induction from Conversations for Task-Oriented Dialogue at the Eleventh Dialogue System Technology Challenge (DSTC11) The essence of intention clustering lies in distinguishing the representation of different dialogue utterances. In the released DSTC11 evaluation results, our proposed system ranked first on both of the two subtasks of this Track.
  arXiv  Detail & Related papers  (2023-03-09T04:51:27Z)
• Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation [48.70062671767362]
  We propose the Variational Reasoning over Incomplete KGs Conversational Recommender (VRICR) Our key idea is to incorporate the large dialogue corpus naturally accompanied with CRSs to enhance the incomplete KGs. We also denote the dialogue-specific subgraphs of KGs as latent variables with categorical priors for adaptive knowledge graphs.
  arXiv  Detail & Related papers  (2022-12-22T17:02:21Z)
• KPT: Keyword-guided Pre-training for Grounded Dialog Generation [82.68787152707455]
  We propose KPT (guided Pre-Training), a novel self-supervised pre-training method for grounded dialog generation. Specifically, we use a pre-trained language model to extract the most uncertain tokens in the dialog as keywords. We conduct extensive experiments on various few-shot knowledge-grounded generation tasks, including grounding on dialog acts, knowledge graphs, persona descriptions, and Wikipedia passages.
  arXiv  Detail & Related papers  (2022-12-04T04:05:01Z)
• There Is No Standard Answer: Knowledge-Grounded Dialogue Generation with Adversarial Activated Multi-Reference Learning [29.093220439736527]
  Knowledge-grounded conversation (KGC) shows excellent potential to deliver an engaging and informative response. Existing approaches emphasize selecting one golden knowledge given a particular dialogue context, overlooking the one-to-many phenomenon in dialogue. We propose a series of metrics to systematically assess the one-to-many efficacy of existing KGC models.
  arXiv  Detail & Related papers  (2022-10-22T14:43:33Z)
• Multimodal Dialog Systems with Dual Knowledge-enhanced Generative Pretrained Language Model [63.461030694700014]
  We propose a novel dual knowledge-enhanced generative pretrained language model for multimodal task-oriented dialog systems (DKMD) The proposed DKMD consists of three key components: dual knowledge selection, dual knowledge-enhanced context learning, and knowledge-enhanced response generation. Experiments on a public dataset verify the superiority of the proposed DKMD over state-of-the-art competitors.
  arXiv  Detail & Related papers  (2022-07-16T13:02:54Z)
• Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning [89.64215566478931]
  Conversational recommender systems (CRS) aim to proactively elicit user preference and recommend high-quality items through natural language conversations. To develop an effective CRS, it is essential to seamlessly integrate the two modules. We propose a unified CRS model named UniCRS based on knowledge-enhanced prompt learning.
  arXiv  Detail & Related papers  (2022-06-19T09:21:27Z)
• Two-Level Supervised Contrastive Learning for Response Selection in Multi-Turn Dialogue [18.668723854662584]
  This paper applies contrastive learning to the problem by using the supervised contrastive loss. We develop a new method for supervised contrastive learning, referred to as two-level supervised contrastive learning.
  arXiv  Detail & Related papers  (2022-03-01T23:43:36Z)
• Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion [77.21442487537139]
  Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. First, the conversation data itself lacks of sufficient contextual information for accurately understanding users' preference. Second, there is a semantic gap between natural language expression and item-level user preference.
  arXiv  Detail & Related papers  (2020-07-08T11:14:23Z)
• Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue [51.513276162736844]
  We propose a sequential latent variable model as the first approach to this matter. The model named sequential knowledge transformer (SKT) can keep track of the prior and posterior distribution over knowledge.
  arXiv  Detail & Related papers  (2020-02-18T11:59:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.