A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems

Abstract

Recent years witnessed several advances in developing multi-goal conversational recommender systems (MG-CRS) that can proactively attract users' interests and naturally lead user-engaged dialogues with multiple conversational goals and diverse topics. Four tasks are often involved in MG-CRS, including Goal Planning, Topic Prediction, Item Recommendation, and Response Generation. Most existing studies address only some of these tasks. To handle the whole problem of MG-CRS, modularized frameworks are adopted where each task is tackled independently without considering their interdependencies. In this work, we propose a novel Unified MultI-goal conversational recommeNDer system, namely UniMIND. In specific, we unify these four tasks with different formulations into the same sequence-to-sequence (Seq2Seq) paradigm. Prompt-based learning strategies are investigated to endow the unified model with the capability of multi-task learning. Finally, the overall learning and inference procedure consists of three stages, including multi-task learning, prompt-based tuning, and inference. Experimental results on two MG-CRS benchmarks (DuRecDial and TG-ReDial) show that UniMIND achieves state-of-the-art performance on all tasks with a unified model. Extensive analyses and discussions are provided for shedding some new perspectives for MG-CRS.

PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance

“…Proactive Conversational Systems Early studies on conversational systems basically develop dialogue systems that passively respond to user queries, including all the CQA studies discussed above. As for conversational recommendation (Lei et al, 2020a,b;Deng et al, 2021) and goal-oriented dialogues Deng et al, 2022b), policy learning or goal planning attaches great importance in building a proactive conversational system for promptly adjusting dialogue strategies or soliciting user intents. Recently, many efforts have been made on CQA systems that can proactively assist users to clarify the ambiguity or uncertainty in their queries by asking clarifying questions (Wang and Li, 2021;Zamani et al, 2020a;Sekulic et al, 2021;Gao and Lam, 2022).…”