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Found 58 papers, 33 papers with code
A Pretraining Numerical Reasoning Model for Ordinal Constrained Question Answering on Knowledge Base
Knowledge Base Question Answering (KBQA) is to answer natural language questions posed over knowledge bases (KBs).
Great Truths are Always Simple: A Rather Simple Knowledge Encoder for Enhancing the Commonsense Reasoning Capacity of Pre-Trained Models
Commonsense reasoning in natural language is a desired ability of artificial intelligent systems.
ElitePLM: An Empirical Study on General Language Ability Evaluation of Pretrained Language Models
In this paper, we present a large-scale empirical study on general language ability evaluation of PLMs (ElitePLM).
Learning to Transfer Prompts for Text Generation
First, PTG learns a set of source prompts for various source generation tasks and then transfers these prompts as target prompts to perform target generation tasks.
Debiased Contrastive Learning of Unsupervised Sentence Representations
In DCLR, we design an instance weighting method to punish false negatives and generate noise-based negatives to guarantee the uniformity of the representation space.
A Thorough Examination on Zero-shot Dense Retrieval
Recent years have witnessed the significant advance in dense retrieval (DR) based on powerful pre-trained language models (PLM).
CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space
Session-based Recommendation (SBR) refers to the task of predicting the next item based on short-term user behaviors within an anonymous session.
Leveraging Search History for Improving Person-Job Fit
As the core technique of online recruitment platforms, person-job fit can improve hiring efficiency by accurately matching job positions with qualified candidates.
WuDaoMM: A large-scale Multi-Modal Dataset for Pre-training models
The results show that WuDaoMM can be applied as an efficient dataset for VLPMs, especially for the model in text-to-image generation task.
Neural Graph Matching for Pre-training Graph Neural Networks
In this way, we can learn adaptive representations for a given graph when paired with different graphs, and both node- and graph-level characteristics are naturally considered in a single pre-training task.
Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models
With the decomposed MPO structure, we can reduce the parameters of the original MoE architecture by sharing a global central tensor across experts and keeping expert-specific auxiliary tensors.
Filter-enhanced MLP is All You Need for Sequential Recommendation
Recently, deep neural networks such as RNN, CNN and Transformer have been applied in the task of sequential recommendation, which aims to capture the dynamic preference characteristics from logged user behavior data for accurate recommendation.
A Survey of Vision-Language Pre-Trained Models
How to adapt pre-training to the field of Vision-and-Language (V-L) learning and improve the performance on downstream tasks becomes a focus of multimodal learning.
Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning
For the structural neighbors on the interaction graph, we develop a novel structure-contrastive objective that regards users (or items) and their structural neighbors as positive contrastive pairs.
Context-Tuning: Learning Contextualized Prompts for Natural Language Generation
Secondly, to further enhance the relevance of the generated text to the inputs, we utilize continuous inverse prompting to refine the process of natural language generation by modeling an inverse generation process from output to input.
Pretrained Language Models for Text Generation: A Survey
We begin with introducing three key aspects of applying PLMs to text generation: 1) how to encode the input into representations preserving input semantics which can be fused into PLMs; 2) how to design an effective PLM to serve as the generation model; and 3) how to effectively optimize PLMs given the reference text and to ensure that the generated texts satisfy special text properties.
C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System
To implement this framework, we design both coarse-grained and fine-grained procedures for modeling user preference, where the former focuses on more general, coarse-grained semantic fusion and the latter focuses on more specific, fine-grained semantic fusion.
LibCity: An Open Library for Traffic Prediction
This paper presents LibCity, a unified, comprehensive, and extensible library for traffic prediction, which provides researchers with a credible experimental tool and a convenient development framework.
RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking
In this paper, we propose a novel joint training approach for dense passage retrieval and passage re-ranking.
Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models
To solve this issue, various data augmentation techniques are proposed to improve the robustness of PLMs.
Complex Knowledge Base Question Answering: A Survey
Knowledge base question answering (KBQA) aims to answer a question over a knowledge base (KB).
PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrieval
Recently, dense passage retrieval has become a mainstream approach to finding relevant information in various natural language processing tasks.
Pre-Trained Models: Past, Present and Future
Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success and become a milestone in the field of artificial intelligence (AI).
Curriculum Pre-Training Heterogeneous Subgraph Transformer for Top-$N$ Recommendation
Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous auxiliary data in top-$N$ recommender systems, called \emph{HIN-based recommendation}.
Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators
This paper presents a novel pre-trained language models (PLM) compression approach based on the matrix product operator (short as MPO) from quantum many-body physics.
Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Models
This paper studies how to automatically generate a natural language text that describes the facts in knowledge graph (KG).
A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions
In this paper, we elaborately summarize the typical challenges and solutions for complex KBQA.
Pretrained Language Models for Text Generation: A Survey
In this paper, we present an overview of the major advances achieved in the topic of PLMs for text generation.
Knowledge-based Review Generation by Coherence Enhanced Text Planning
For global coherence, we design a hierarchical self-attentive architecture with both subgraph- and node-level attention to enhance the correlations between subgraphs.
WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training
We further construct a large Chinese multi-source image-text dataset called RUC-CAS-WenLan for pre-training our BriVL model.
Ranked #1 on
Image-to-Text Retrieval
on AIC-ICC
Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals
In our approach, the student network aims to find the correct answer to the query, while the teacher network tries to learn intermediate supervision signals for improving the reasoning capacity of the student network.
Ranked #1 on
Semantic Parsing
on WebQuestionsSP
TextBox: A Unified, Modularized, and Extensible Framework for Text Generation
In this paper, we release an open-source library, called TextBox, to provide a unified, modularized, and extensible text generation framework.
CRSLab: An Open-Source Toolkit for Building Conversational Recommender System
In recent years, conversational recommender system (CRS) has received much attention in the research community.
RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms
In this library, we implement 73 recommendation models on 28 benchmark datasets, covering the categories of general recommendation, sequential recommendation, context-aware recommendation and knowledge-based recommendation.
RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering
In open-domain question answering, dense passage retrieval has become a new paradigm to retrieve relevant passages for finding answers.
Ranked #3 on
Passage Retrieval
on Natural Questions
Revisiting Alternative Experimental Settings for Evaluating Top-N Item Recommendation Algorithms
Top-N item recommendation has been a widely studied task from implicit feedback.
Towards Topic-Guided Conversational Recommender System
To develop an effective CRS, the support of high-quality datasets is essential.
Knowledge-Enhanced Personalized Review Generation with Capsule Graph Neural Network
First, based on graph capsules, we adaptively learn aspect capsules for inferring the aspect sequence.
Learning to Match Jobs with Resumes from Sparse Interaction Data using Multi-View Co-Teaching Network
Compared with pure text-based matching models, the proposed approach is able to learn better data representations from limited or even sparse interaction data, which is more resistible to noise in training data.
Leveraging Historical Interaction Data for Improving Conversational Recommender System
Most of the existing CRS methods focus on learning effective preference representations for users from conversation data alone.
S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
To tackle this problem, we propose the model S^3-Rec, which stands for Self-Supervised learning for Sequential Recommendation, based on the self-attentive neural architecture.
Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion
Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations.
Sequential Recommendation with Self-Attentive Multi-Adversarial Network
Recently, deep learning has made significant progress in the task of sequential recommendation.
Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning
Our generator is isolated from user interaction data, and serves to improve the performance of the discriminator.
Improving Multi-Turn Response Selection Models with Complementary Last-Utterance Selection by Instance Weighting
Open-domain retrieval-based dialogue systems require a considerable amount of training data to learn their parameters.
A Neural Citation Count Prediction Model based on Peer Review Text
An important kind of data signals, peer review text, has not been utilized for the CCP task.
Domain Adaptation for Person-Job Fit with Transferable Deep Global Match Network
Furthermore, we extend the match network and implement domain adaptation in three levels, sentence-level representation, sentence-level match, and global match.
Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation
To address these issues, we propose using neural networks to automatically learn the cost functions of a classic heuristic algorithm, namely A* algorithm, for the PRR task.
Generating Long and Informative Reviews with Aspect-Aware Coarse-to-Fine Decoding
In this paper, we propose a novel review generation model by characterizing an elaborately designed aspect-aware coarse-to-fine generation process.
A Long-Short Demands-Aware Model for Next-Item Recommendation
Recommending the right products is the central problem in recommender systems, but the right products should also be recommended at the right time to meet the demands of users, so as to maximize their values.
KB4Rec: A Dataset for Linking Knowledge Bases with Recommender Systems
Based on our linked dataset, we first preform some interesting qualitative analysis experiments, in which we discuss the effect of two important factors (i. e. popularity and recency) on whether a RS item can be linked to a KB entity.
Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network
Human generates responses relying on semantic and functional dependencies, including coreference relation, among dialogue elements and their context.
Ranked #6 on
Conversational Response Selection
on RRS
hyperdoc2vec: Distributed Representations of Hypertext Documents
Hypertext documents, such as web pages and academic papers, are of great importance in delivering information in our daily life.
Heterogeneous Information Network Embedding for Recommendation
In this paper, we propose a novel heterogeneous network embedding based approach for HIN based recommendation, called HERec.
Social and Information Networks
A General SIMD-based Approach to Accelerating Compression Algorithms
Compression algorithms are important for data oriented tasks, especially in the era of Big Data.
