no code implementations • 21 Feb 2024 • Wanqing Cui, Keping Bi, Jiafeng Guo, Xueqi Cheng
Since commonsense information has been recorded significantly less frequently than its existence, language models pre-trained by text generation have difficulty to learn sufficient commonsense knowledge.
1 code implementation • 18 Feb 2024 • Shiyu Ni, Keping Bi, Jiafeng Guo, Xueqi Cheng
This motivates us to enhance the LLMs' ability to perceive their knowledge boundaries to help RA.
1 code implementation • 8 Jan 2024 • Keping Bi, Xiaojie Sun, Jiafeng Guo, Xueqi Cheng
MADRAL was evaluated on proprietary data and its code was not released, making it challenging to validate its effectiveness on other datasets.
1 code implementation • 5 Dec 2023 • Xiaojie Sun, Keping Bi, Jiafeng Guo, Sihui Yang, Qishen Zhang, Zhongyi Liu, Guannan Zhang, Xueqi Cheng
Dense retrieval methods have been mostly focused on unstructured text and less attention has been drawn to structured data with various aspects, e. g., products with aspects such as category and brand.
no code implementations • 6 Nov 2023 • Yinqiong Cai, Yixing Fan, Keping Bi, Jiafeng Guo, Wei Chen, Ruqing Zhang, Xueqi Cheng
The first-stage retrieval aims to retrieve a subset of candidate documents from a huge collection both effectively and efficiently.
no code implementations • 18 Oct 2023 • Lulu Yu, Keping Bi, Jiafeng Guo, Xueqi Cheng
The Chinese academy of sciences Information Retrieval team (CIR) has participated in the NTCIR-17 ULTRE-2 task.
1 code implementation • 1 Oct 2023 • Shiyu Ni, Keping Bi, Jiafeng Guo, Xueqi Cheng
In this paper, we aim to conduct a systematic comparative study of various types of training objectives, with different properties of not only whether it is permutation-invariant but also whether it conducts sequential prediction and whether it can control the count of output facets.
1 code implementation • 22 Aug 2023 • Yinqiong Cai, Keping Bi, Yixing Fan, Jiafeng Guo, Wei Chen, Xueqi Cheng
First-stage retrieval is a critical task that aims to retrieve relevant document candidates from a large-scale collection.
1 code implementation • 22 Aug 2023 • Xiaojie Sun, Keping Bi, Jiafeng Guo, Xinyu Ma, Fan Yixing, Hongyu Shan, Qishen Zhang, Zhongyi Liu
Extensive experiments on two real-world datasets (product and mini-program search) show that our approach can outperform competitive baselines both treating aspect values as classes and conducting the same MLM for aspect and content strings.
no code implementations • 18 Feb 2023 • Xiaojie Sun, Lulu Yu, Yiting Wang, Keping Bi, Jiafeng Guo
Then we fine-tune several pre-trained models and train an ensemble model to aggregate all the predictions from various pre-trained models with human-annotation data in the fine-tuning stage.
no code implementations • 15 Feb 2023 • Lulu Yu, Yiting Wang, Xiaojie Sun, Keping Bi, Jiafeng Guo
Unbiased learning to rank (ULTR) aims to mitigate various biases existing in user clicks, such as position bias, trust bias, presentation bias, and learn an effective ranker.
no code implementations • 12 Jul 2021 • Keping Bi, Qingyao Ai, W. Bruce Croft
To quickly identify user intent and reduce effort during interactions, we propose an intent clarification task based on yes/no questions where the system needs to ask the correct question about intents within the fewest conversation turns.
no code implementations • 15 Feb 2021 • Keping Bi, Pavel Metrikov, Chunyuan Li, Byungki Byun
Given these observations, we propose to leverage user search history as query context to characterize users and build a context-aware ranking model for email search.
no code implementations • 18 May 2020 • Keping Bi, Qingyao Ai, W. Bruce Croft
Aware of these limitations, we propose a transformer-based embedding model (TEM) for personalized product search, which could dynamically control the influence of personalization by encoding the sequence of query and user's purchase history with a transformer architecture.
no code implementations • EMNLP (Eval4NLP) 2020 • Rahul Jha, Keping Bi, Yang Li, Mahdi Pakdaman, Asli Celikyilmaz, Ivan Zhiboedov, Kieran McDonald
We describe the annotation process in detail and compare it with other similar evaluation systems.
1 code implementation • 20 Apr 2020 • Keping Bi, Qingyao Ai, W. Bruce Croft
RTM conducts review-level matching between the user and item, where each review has a dynamic effect according to the context in the sequence.
3 code implementations • EACL 2021 • Keping Bi, Rahul Jha, W. Bruce Croft, Asli Celikyilmaz
Redundancy-aware extractive summarization systems score the redundancy of the sentences to be included in a summary either jointly with their salience information or separately as an additional sentence scoring step.
no code implementations • 16 Sep 2019 • Qingyao Ai, Yongfeng Zhang, Keping Bi, W. Bruce Croft
Specifically, we propose to model the "search and purchase" behavior as a dynamic relation between users and items, and create a dynamic knowledge graph based on both the multi-relational product data and the context of the search session.
no code implementations • 9 Sep 2019 • Keping Bi, Choon Hui Teo, Yesh Dattatreya, Vijai Mohan, W. Bruce Croft
In this paper, we study RF techniques based on both long-term and short-term context dependencies in multi-page product search.
no code implementations • 4 Sep 2019 • Keping Bi, Qingyao Ai, Yongfeng Zhang, W. Bruce Croft
So in this paper, we propose a conversational paradigm for product search driven by non-relevant items, based on which fine-grained feedback is collected and utilized to show better results in the next iteration.
no code implementations • 4 Sep 2019 • Keping Bi, Choon Hui Teo, Yesh Dattatreya, Vijai Mohan, W. Bruce Croft
However, customers with little or no purchase history do not benefit from personalized product search.
no code implementations • 13 Dec 2018 • Keping Bi, Qingyao Ai, W. Bruce Croft
We conduct extensive experiments to analyze and compare IRF with the standard top-k RF framework on document and passage retrieval.
1 code implementation • 16 Apr 2018 • Qingyao Ai, Keping Bi, Cheng Luo, Jiafeng Guo, W. Bruce Croft
We find that the problem of estimating a propensity model from click data is a dual problem of unbiased learning to rank.
1 code implementation • 16 Apr 2018 • Qingyao Ai, Keping Bi, Jiafeng Guo, W. Bruce Croft
Specifically, we employ a recurrent neural network to sequentially encode the top results using their feature vectors, learn a local context model and use it to re-rank the top results.