Pengjie Ren

RefNet: A Reference-aware Network for Background Based Conversation

2019 · arXiv (Cornell University)

Existing conversational systems tend to generate generic responses. Recently, Background Based Conversations (BBCs) have been introduced to address this issue. Here, the generated responses are grounded in some background information. The proposed methods for BBCs …

Attribute-aware Diversification for Sequential Recommendations

2020 · UvA-DARE (University of Amsterdam)

Users prefer diverse recommendations over homogeneous ones. However, most previous work on Sequential Recommenders does not consider diversity, and strives for maximum accuracy, resulting in homogeneous recommendations. In this paper, we consider both accuracy and …

Diversifying Task-oriented Dialogue Response Generation with Prototype Guided Paraphrasing

2020 · arXiv (Cornell University)

Existing methods for Dialogue Response Generation (DRG) in Task-oriented Dialogue Systems (TDSs) can be grouped into two categories: template-based and corpus-based. The former prepare a collection of response templates in advance and fill the slots …

Contrastive Learning Reduces Hallucination in Conversations

2022 · arXiv (Cornell University)

Pre-trained language models (LMs) store knowledge in their parameters and can generate informative responses when used in conversational systems. However, LMs suffer from the problem of "hallucination:" they may generate plausible-looking statements that are irrelevant …

Generative Retrieval as Multi-Vector Dense Retrieval

2024 · arXiv (Cornell University)

Generative retrieval generates identifiers of relevant documents in an end-to-end manner using a sequence-to-sequence architecture for a given query. The relation between generative retrieval and other retrieval methods, especially those based on matching within dense …

Uncovering Overfitting in Large Language Model Editing

2024 · arXiv (Cornell University)

Knowledge editing has been proposed as an effective method for updating and correcting the internal knowledge of Large Language Models (LLMs). However, existing editing methods often struggle with complex tasks, such as multi-hop reasoning. In …

A Collaborative Session-based Recommendation Approach with Parallel Memory Modules

2019

Session-based recommendation is the task of predicting the next item to recommend when the only available information consists of anonymous behavior sequences. Previous methods for session-based recommendation focus mostly on the current session, ignoring collaborative …

RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-Based Recommendation

2019 · Proceedings of the AAAI Conference on Artificial Intelligence

Recurrent neural networks for session-based recommendation have attracted a lot of attention recently because of their promising performance. repeat consumption is a common phenomenon in many recommendation scenarios (e.g., e-commerce, music, and TV program recommendations), …

Neural Attentive Session-based Recommendation

2017

Given e-commerce scenarios that user profiles are invisible, session-based recommendation is proposed to generate recommendation results from short sessions. Previous work only considers the user's sequential behavior in the current session, whereas the user's main …

Query Resolution for Conversational Search with Limited Supervision

2020

In this work we focus on multi-turn passage retrieval as a crucial component of conversational search. One of the key challenges in multi-turn passage retrieval comes from the fact that the current turn query is …

Mixed Information Flow for Cross-Domain Sequential Recommendations

2022 · ACM Transactions on Knowledge Discovery from Data

Cross-domain sequential recommendation is the task of predict the next item that the user is most likely to interact with based on past sequential behavior from multiple domains. One of the key challenges in cross-domain …