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Found 172 papers, 105 papers with code
Optimal Partial Transport Based Sentence Selection for Long-form Document Matching
Enjoying the partial transport properties of OPT, the selected key sentences can not only effectively enhance the matching accuracy, but also be explained as the rationales for the matching results.
Finding the Dominant Winning Ticket in Pre-Trained Language Models
Empirically, we show that (a) the dominant winning ticket can achieve performance that is comparable with that of the full-parameter model, (b) the dominant winning ticket is transferable across different tasks, (c) and the dominant winning ticket has a natural structure within each parameter matrix.
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.
Semantic Sentence Matching via Interacting Syntax Graphs
In this paper, we formalize the task of semantic sentence matching as a problem of graph matching in which each sentence is represented as a directed graph according to its syntactic structures.
There Are a Thousand Hamlets in a Thousand People’s Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory
Knowledge-grounded conversation (KGC) shows great potential in building an engaging and knowledgeable chatbot, and knowledge selection is a key ingredient in it.
Continual Pre-training of Language Models for Math Problem Understanding with Syntax-Aware Memory Network
In this paper, we study how to continually pre-train language models for improving the understanding of math problems.
Optimizing Factual Accuracy in Text Generation through Dynamic Knowledge Selection
Previous methods use external knowledge as references for text generation to enhance factuality but often struggle with the knowledge mix-up(e. g., entity mismatch) of irrelevant references.
A Survey on Large Language Model based Autonomous Agents
In this paper, we present a comprehensive survey of these studies, delivering a systematic review of the field of LLM-based autonomous agents from a holistic perspective.
Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation
In the video recommendation, watch time is commonly adopted as an indicator of user interest.
Large Language Models for Information Retrieval: A Survey
This evolution requires a combination of both traditional methods (such as term-based sparse retrieval methods with rapid response) and modern neural architectures (such as language models with powerful language understanding capacity).
LTP-MMF: Towards Long-term Provider Max-min Fairness Under Recommendation Feedback Loops
RFL means that recommender system can only receive feedback on exposed items from users and update recommender models incrementally based on this feedback.
Counterfactual Cross-modality Reasoning for Weakly Supervised Video Moment Localization
Finally, by suppressing the unimodal effect of masked query, we can rectify the reconstructions of video proposals to perform reasonable contrastive learning.
Synthesizing Long-Term Human Motions with Diffusion Models via Coherent Sampling
Firstly, they cannot directly generate coherent motions and require additional operations such as interpolation to process the generated actions.
Spatio-Temporal Branching for Motion Prediction using Motion Increments
Human motion prediction (HMP) has emerged as a popular research topic due to its diverse applications, but it remains a challenging task due to the stochastic and aperiodic nature of future poses.
Alleviating the Long-Tail Problem in Conversational Recommender Systems
Conversational recommender systems (CRS) aim to provide the recommendation service via natural language conversations.
Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation
In this study, we present an initial analysis of the factual knowledge boundaries of LLMs and how retrieval augmentation affects LLMs on open-domain QA.
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs.
Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study
Different from previous studies focused on overall performance, this work aims to investigate the impact of quantization on \emph{emergent abilities}, which are important characteristics that distinguish LLMs from small language models.
SSP: Self-Supervised Post-training for Conversational Search
Conversational search has been regarded as the next-generation search paradigm.
JiuZhang 2.0: A Unified Chinese Pre-trained Language Model for Multi-task Mathematical Problem Solving
Specially, we construct a Mixture-of-Experts~(MoE) architecture for modeling mathematical text, so as to capture the common mathematical knowledge across tasks.
RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit
To support research in this area and facilitate the development of retrieval-augmented LLM systems, we develop RETA-LLM, a {RET}reival-{A}ugmented LLM toolkit.
Improving Conversational Recommendation Systems via Counterfactual Data Simulation
To develop our approach, we characterize user preference and organize the conversation flow by the entities involved in the dialogue, and design a multi-stage recommendation dialogue simulator based on a conversation flow language model.
When Large Language Model based Agent Meets User Behavior Analysis: A Novel User Simulation Paradigm
We argue that these models present significant opportunities for reliable user simulation, and have the potential to revolutionize traditional study paradigms in user behavior analysis.
Evaluating and Improving Tool-Augmented Computation-Intensive Math Reasoning
Based on this finding, we propose a new approach that can deliberate the reasoning steps with tool interfaces, namely \textbf{DELI}.
Zero-shot Visual Question Answering with Language Model Feedback
In this paper, we propose a novel language model guided captioning approach, LAMOC, for knowledge-based visual question answering (VQA).
Learning to Imagine: Visually-Augmented Natural Language Generation
People often imagine relevant scenes to aid in the writing process.
ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models
To improve the reasoning abilities, we propose \textbf{ChatCoT}, a tool-augmented chain-of-thought reasoning framework for chat-based LLMs.
Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models
The recent success of large language models (LLMs) has shown great potential to develop more powerful conversational recommender systems (CRSs), which rely on natural language conversations to satisfy user needs.
HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models
To understand what types of content and to which extent LLMs are apt to hallucinate, we introduce the Hallucination Evaluation for Large Language Models (HaluEval) benchmark, a large collection of generated and human-annotated hallucinated samples for evaluating the performance of LLMs in recognizing hallucination.
When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation
In our paper, we propose a Search-Enhanced framework for the Sequential Recommendation (SESRec) that leverages users' search interests for recommendation, by disentangling similar and dissimilar representations within S&R behaviors.
The Web Can Be Your Oyster for Improving Large Language Models
In order to further improve the capacity of LLMs for knowledge-intensive tasks, we consider augmenting LLMs with the large-scale web using search engine.
TOME: A Two-stage Approach for Model-based Retrieval
Recently, model-based retrieval has emerged as a new paradigm in text retrieval that discards the index in the traditional retrieval model and instead memorizes the candidate corpora using model parameters.
Evaluating Object Hallucination in Large Vision-Language Models
Despite the promising progress on LVLMs, we find that LVLMs suffer from the hallucination problem, i. e. they tend to generate objects that are inconsistent with the target images in the descriptions.
StructGPT: A General Framework for Large Language Model to Reason over Structured Data
Specially, we propose an \emph{invoking-linearization-generation} procedure to support LLMs in reasoning on the structured data with the help of the external interfaces.
Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach
Inspired by the recent progress on large language models (LLMs), we take a different approach to developing the recommendation models, considering recommendation as instruction following by LLMs.
Diffusion-NAT: Self-Prompting Discrete Diffusion for Non-Autoregressive Text Generation
To solve it, we propose Diffusion-NAT, which introduces discrete diffusion models~(DDM) into NAR text-to-text generation and integrates BART to improve the performance.
GlyphDiffusion: Text Generation as Image Generation
In this way, conditional text generation can be cast as a glyph image generation task, and it is then natural to apply continuous diffusion models to discrete texts.
EulerNet: Adaptive Feature Interaction Learning via Euler's Formula for CTR Prediction
EulerNet converts the exponential powers of feature interactions into simple linear combinations of the modulus and phase of the complex features, making it possible to adaptively learn the high-order feature interactions in an efficient way.
WebBrain: Learning to Generate Factually Correct Articles for Queries by Grounding on Large Web Corpus
In this paper, we introduce a new NLP task -- generating short factual articles with references for queries by mining supporting evidence from the Web.
A Survey of Large Language Models
To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
Scaling Pre-trained Language Models to Deeper via Parameter-efficient Architecture
Based on such a decomposition, our architecture shares the central tensor across all layers for reducing the model size and meanwhile keeps layer-specific auxiliary tensors (also using adapters) for enhancing the adaptation flexibility.
Dually Enhanced Propensity Score Estimation in Sequential Recommendation
According to the graph, two complementary propensity scores are estimated from the views of item and user, respectively, based on the same set of user feedback data.
Diffusion Models for Non-autoregressive Text Generation: A Survey
In this survey, we review the recent progress in diffusion models for NAR text generation.
REASONER: An Explainable Recommendation Dataset with Multi-aspect Real User Labeled Ground Truths Towards more Measurable Explainable Recommendation
To alleviate the above problems, we propose to build an explainable recommendation dataset with multi-aspect real user labeled ground truths.
TextBox 2.0: A Text Generation Library with Pre-trained Language Models
To facilitate research on text generation, this paper presents a comprehensive and unified library, TextBox 2. 0, focusing on the use of pre-trained language models (PLMs).
Ranked #1 on
Style Transfer
on GYAFC
Visually-augmented pretrained language models for NLP tasks without images
Although pre-trained language models~(PLMs) have shown impressive performance by text-only self-supervised training, they are found lack of visual semantics or commonsense.
MASTER: Multi-task Pre-trained Bottlenecked Masked Autoencoders are Better Dense Retrievers
Pre-trained Transformers (\eg BERT) have been commonly used in existing dense retrieval methods for parameter initialization, and recent studies are exploring more effective pre-training tasks for further improving the quality of dense vectors.
UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph
Multi-hop Question Answering over Knowledge Graph~(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question on a large-scale Knowledge Graph (KG).
CDSM: Cascaded Deep Semantic Matching on Textual Graphs Leveraging Ad-hoc Neighbor Selection
In the first stage, a lightweight CNN-based ad-hod neighbor selector is deployed to filter useful neighbors for the matching task with a small computation cost.
Recent Advances in RecBole: Extensions with more Practical Considerations
In order to show the recent update in RecBole, we write this technical report to introduce our latest improvements on RecBole.
Dense Text Retrieval based on Pretrained Language Models: A Survey
With powerful PLMs, we can effectively learn the representations of queries and texts in the latent representation space, and further construct the semantic matching function between the dense vectors for relevance modeling.
Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation
Some recent knowledge distillation based methods transfer knowledge from complex teacher models to shallow student models for accelerating the online model inference.
ELMER: A Non-Autoregressive Pre-trained Language Model for Efficient and Effective Text Generation
However, NAR models usually generate texts of lower quality due to the absence of token dependency in the output text.
SimANS: Simple Ambiguous Negatives Sampling for Dense Text Retrieval
Thus, we propose a simple ambiguous negatives sampling method, SimANS, which incorporates a new sampling probability distribution to sample more ambiguous negatives.
Privacy-Preserved Neural Graph Similarity Learning
To develop effective and efficient graph similarity learning (GSL) models, a series of data-driven neural algorithms have been proposed in recent years.
Law Article-Enhanced Legal Case Matching: a Causal Learning Approach
We show that the framework is model-agnostic, and a number of legal case matching models can be applied as the underlying models.
Partial Information as Full: Reward Imputation with Sketching in Bandits
In this paper, we propose an efficient reward imputation approach using sketching for CBB, which completes the unobserved rewards with the imputed rewards approximating the full-information feedbacks.
Modeling Multiple Views via Implicitly Preserving Global Consistency and Local Complementarity
To this end, we propose a methodology, specifically consistency and complementarity network (CoCoNet), which avails of strict global inter-view consistency and local cross-view complementarity preserving regularization to comprehensively learn representations from multiple views.
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language
Although artificial intelligence (AI) has made significant progress in understanding molecules in a wide range of fields, existing models generally acquire the single cognitive ability from the single molecular modality.
Ranked #3 on
Molecule Captioning
on ChEBI-20
Enhancing User Behavior Sequence Modeling by Generative Tasks for Session Search
To help the encoding of the current user behavior sequence, we propose to use a decoder and the information of future sequences and a supplemental query.
Ultron: An Ultimate Retriever on Corpus with a Model-based Indexer
In order to unify these two stages, we explore a model-based indexer for document retrieval.
Modeling Two-Way Selection Preference for Person-Job Fit
To model the two-way selection preference from the dual-perspective of job seekers and employers, we incorporate two different nodes for each candidate (or job) and characterize both successful matching and failed matching via a unified dual-perspective interaction graph.
Multimodal foundation models are better simulators of the human brain
Further, from the perspective of neural encoding (based on our foundation model), we find that both visual and lingual encoders trained multimodally are more brain-like compared with unimodal ones.
STAR-GNN: Spatial-Temporal Video Representation for Content-based Retrieval
It models a video with a lattice feature graph in which the nodes represent regions of different granularity, with weighted edges that represent the spatial and temporal links.
Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction
As an essential operation of legal retrieval, legal case matching plays a central role in intelligent legal systems.
MVP: Multi-task Supervised Pre-training for Natural Language Generation
Motivated by the success of supervised pre-training, we propose Multi-task superVised Pre-training (MVP) for natural language generation.
Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning
Our approach unifies the recommendation and conversation subtasks into the prompt learning paradigm, and utilizes knowledge-enhanced prompts based on a fixed pre-trained language model (PLM) to fulfill both subtasks in a unified approach.
Ranked #1 on
Text Generation
on ReDial
RecBole 2.0: Towards a More Up-to-Date Recommendation Library
In order to support the study of recent advances in recommender systems, this paper presents an extended recommendation library consisting of eight packages for up-to-date topics and architectures.
JiuZhang: A Chinese Pre-trained Language Model for Mathematical Problem Understanding
Considering the complex nature of mathematical texts, we design a novel curriculum pre-training approach for improving the learning of mathematical PLMs, consisting of both basic and advanced courses.
Towards Universal Sequence Representation Learning for Recommender Systems
In order to develop effective sequential recommenders, a series of sequence representation learning (SRL) methods are proposed to model historical user behaviors.
Feature-aware Diversified Re-ranking with Disentangled Representations for Relevant Recommendation
Relevant recommendation is a special recommendation scenario which provides relevant items when users express interests on one target item (e. g., click, like and purchase).
Negative Sampling for Contrastive Representation Learning: A Review
The learn-to-compare paradigm of contrastive representation learning (CRL), which compares positive samples with negative ones for representation learning, has achieved great success in a wide range of domains, including natural language processing, computer vision, information retrieval and graph learning.
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.
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.
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).
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).
Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation
Existing personalized dialogue systems have tried to extract user profiles from dialogue history to guide personalized response generation.
COTS: Collaborative Two-Stream Vision-Language Pre-Training Model for Cross-Modal Retrieval
Under a fair comparison setting, our COTS achieves the highest performance among all two-stream methods and comparable performance (but with 10, 800X faster in inference) w. r. t.
Ranked #15 on
Video Retrieval
on MSR-VTT
There Are a Thousand Hamlets in a Thousand People's Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory
In this work, we introduce personal memory into knowledge selection in KGC to address the personalization issue.
Unbiased Top-k Learning to Rank with Causal Likelihood Decomposition
An extension to the pairwise neural ranking is also developed.
Sequential Recommendation with Causal Behavior Discovery
The key of sequential recommendation lies in the accurate item correlation modeling.
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.
Learning to Answer Questions in Dynamic Audio-Visual Scenarios
In this paper, we focus on the Audio-Visual Question Answering (AVQA) task, which aims to answer questions regarding different visual objects, sounds, and their associations in videos.
Ranked #4 on
Audio-visual Question Answering
on MUSIC-AVQA
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation
Applying existing methods to emotional support conversation -- which provides valuable assistance to people who are in need -- has two major limitations: (a) they generally employ a conversation-level emotion label, which is too coarse-grained to capture user's instant mental state; (b) most of them focus on expressing empathy in the response(s) rather than gradually reducing user's distress.
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
Recently, Mixture-of-Experts (short as MoE) architecture has achieved remarkable success in increasing the model capacity of large-scale language models.
DynamicRetriever: A Pre-training Model-based IR System with Neither Sparse nor Dense Index
Web search provides a promising way for people to obtain information and has been extensively studied.
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.
Measuring "Why" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendation
Beyond summarizing the previous work, we also analyze the (dis)advantages of existing evaluation methods and provide a series of guidelines on how to select them.
A Model-Agnostic Causal Learning Framework for Recommendation using Search Data
In this paper, we propose a model-agnostic framework named IV4Rec that can effectively decompose the embedding vectors into these two parts, hence enhancing recommendation results.
Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited
GPR-GNN and BernNet demonstrate that the Monomial and Bernstein bases also outperform the Chebyshev basis in terms of learning the spectral graph convolutions.
Context-Tuning: Learning Contextualized Prompts for Natural Language Generation
Secondly, we use continuous inverse prompting to improve the process of natural language generation by modeling an inverse generation process from output to input, making the generated text more relevant to the inputs.
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.
Class-aware Sounding Objects Localization via Audiovisual Correspondence
To address this problem, we propose a two-stage step-by-step learning framework to localize and recognize sounding objects in complex audiovisual scenarios using only the correspondence between audio and vision.
Compressed Video Contrastive Learning
To enhance the representation ability of the motion vectors, hence the effectiveness of our method, we design a cross guidance contrastive learning algorithm based on multi-instance InfoNCE loss, where motion vectors can take supervision signals from RGB frames and vice versa.
PSSL: Self-supervised Learning for Personalized Search with Contrastive Sampling
Personalized search plays a crucial role in improving user search experience owing to its ability to build user profiles based on historical behaviors.
Towards artificial general intelligence via a multimodal foundation model
To overcome this limitation and take a solid step towards artificial general intelligence (AGI), we develop a foundation model pre-trained with huge multimodal data, which can be quickly adapted for various downstream cognitive tasks.
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.
USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequence
Search and recommendation are the two most common approaches used by people to obtain information.
Image Dataset Compression Based on Matrix Product States
Based on the MPS structure, we propose a new dataset compression method that compresses datasets by filtering long-range correlation information in task-agnostic scenarios and uses dataset distillation to supplement the information in task-specific scenarios.
Log-Polar Space Convolution
Convolutional neural networks use regular quadrilateral convolution kernels to extract features.
Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification
Explainable distances for sequence data depend on temporal alignment to tackle sequences with different lengths and local variances.
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.
One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User Profiles
Specifically, leveraging the benefits of Transformer on language understanding, we train a personalized language model to construct a general user profile from the user's historical responses.
Pre-training for Ad-hoc Retrieval: Hyperlink is Also You Need
In this paper, we propose to leverage the large-scale hyperlinks and anchor texts to pre-train the language model for ad-hoc retrieval.
Learning Implicit User Profiles for Personalized Retrieval-Based Chatbot
To learn a user's personalized language style, we elaborately build language models from shallow to deep using the user's historical responses; To model a user's personalized preferences, we explore the conditional relations underneath each post-response pair of the user.
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.
Modeling Relevance Ranking under the Pre-training and Fine-tuning Paradigm
Experimental results base on three publicly available benchmarks showed that in both of the implementations, Pre-Rank can respectively outperform the underlying ranking models and achieved state-of-the-art performances.
Self-supervised Audiovisual Representation Learning for Remote Sensing Data
By fine-tuning the models on a number of commonly used remote sensing datasets, we show that our approach outperforms existing pre-training strategies for remote sensing imagery.
A Pre-training Strategy for Zero-Resource Response Selection in Knowledge-Grounded Conversations
Recently, many studies are emerging towards building a retrieval-based dialogue system that is able to effectively leverage background knowledge (e. g., documents) when conversing with humans.
Log-Polar Space Convolution for Convolutional Neural Networks
Convolutional neural networks use regular quadrilateral convolution kernels to extract features.
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}.
A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech
A second (multi-relational) GCN is then applied to the utterance states to produce a discourse relation-augmented representation for the utterances, which are then fused together with token states in each utterance as input to a dropped pronoun recovery layer.
Ranked #5 on
Discourse Parsing
on STAC
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 Retrieval
on RUC-CAS-WenLan
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 #2 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.
Self-Supervised Video Representation Learning with Constrained Spatiotemporal Jigsaw
With the constrained jigsaw puzzles, instead of solving them directly, which could still be extremely hard, we carefully design four surrogate tasks that are more solvable but meanwhile still ensure that the learned representation is sensitive to spatiotemporal continuity at both the local and global levels.
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.
Scalable Graph Neural Networks via Bidirectional Propagation
Most notably, GBP can deliver superior performance on a graph with over 60 million nodes and 1. 8 billion edges in less than half an hour on a single machine.
Wasserstein Distance Regularized Sequence Representation for Text Matching in Asymmetrical Domains
Four popular text matching methods have been exploited in the paper.
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.
Transformer-GCRF: Recovering Chinese Dropped Pronouns with General Conditional Random Fields
Exploratory analysis also demonstrates that the GCRF did help to capture the dependencies between pronouns in neighboring utterances, thus contributes to the performance improvements.
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.
Pchatbot: A Large-Scale Dataset for Personalized Chatbot
This enables the development of personalized dialogue models that directly learn implicit user personality from the user's dialogue history.
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.
Variance Reduction for Deep Q-Learning using Stochastic Recursive Gradient
Deep Q-learning algorithms often suffer from poor gradient estimations with an excessive variance, resulting in unstable training and poor sampling efficiency.
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.
Ranked #3 on
Text Generation
on ReDial
Counterfactual VQA: A Cause-Effect Look at Language Bias
VQA models may tend to rely on language bias as a shortcut and thus fail to sufficiently learn the multi-modal knowledge from both vision and language.
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.
Domain-Adaptive Few-Shot Learning
Existing few-shot learning (FSL) methods make the implicit assumption that the few target class samples are from the same domain as the source class samples.
AdarGCN: Adaptive Aggregation GCN for Few-Shot Learning
To address this problem, we propose a graph convolutional network (GCN)-based label denoising (LDN) method to remove the irrelevant images.
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.
Meta-Learning across Meta-Tasks for Few-Shot Learning
In this paper, we argue that the inter-meta-task relationships should be exploited and those tasks are sampled strategically to assist in meta-learning.
Few-Shot Learning as Domain Adaptation: Algorithm and Analysis
Specifically, armed with a set transformer based attention module, we construct each episode with two sub-episodes without class overlap on the seen classes to simulate the domain shift between the seen and unseen classes.
SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval
In learning-to-rank for information retrieval, a ranking model is automatically learned from the data and then utilized to rank the sets of retrieved documents.