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Found 18 papers, 16 papers with code
CaseLink: Inductive Graph Learning for Legal Case Retrieval
In a case pool, there are three types of case connectivity relationships: the case reference relationship, the case semantic relationship, and the case legal charge relationship.
PUMA: Efficient Continual Graph Learning with Graph Condensation
Our prior work, CaT is a replay-based framework with a balanced continual learning procedure, which designs a small yet effective memory bank for replaying data by condensing incoming graphs.
CaseGNN: Graph Neural Networks for Legal Case Retrieval with Text-Attributed Graphs
Previous neural legal case retrieval models mostly encode the unstructured raw text of case into a case representation, which causes the lack of important legal structural information in a case and leads to poor case representation; (2) Lengthy legal text limitation.
CaT: Balanced Continual Graph Learning with Graph Condensation
Recent replay-based methods intend to solve this problem by updating the model using both (1) the entire new-coming data and (2) a sampling-based memory bank that stores replayed graphs to approximate the distribution of historical data.
Balanced and Explainable Social Media Analysis for Public Health with Large Language Models
Furthermore, an LLMs explanation mechanism is proposed by prompting an LLM with the predicted results from BERT models.
UQ at #SMM4H 2023: ALEX for Public Health Analysis with Social Media
As social media becomes increasingly popular, more and more activities related to public health emerge.
Prompt-based Effective Input Reformulation for Legal Case Retrieval
Although these straightforward methods have achieved improvement over conventional statistical methods in retrieval accuracy, two significant challenges are identified in this paper: (1) Legal feature alignment: the usage of the whole case text as the input will generally incorporate redundant and noisy information because, from the legal perspective, the determining factor of relevant cases is the alignment of key legal features instead of whole text matching; (2) Legal context preservation: furthermore, since the existing text encoding models usually have an input length limit shorter than the case, the whole case text needs to be truncated or divided into paragraphs, which leads to the loss of the global context of legal information.
Beyond Double Ascent via Recurrent Neural Tangent Kernel in Sequential Recommendation
In this paper, we propose the Overparameterised Recommender (OverRec), which utilises a recurrent neural tangent kernel (RNTK) as a similarity measurement for user sequences to successfully bypass the restriction of hardware for huge models.
Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation
In this paper, both empirical and theoretical investigations of this representation degeneration problem are first provided, based on which a novel recommender model DuoRec is proposed to improve the item embeddings distribution.
Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation
In this paper, we propose a novel sequential recommendation framework to overcome these challenges based on a memory augmented multi-instance contrastive predictive coding scheme, denoted as MMInfoRec.
Learning to Diversify for Single Domain Generalization
Domain generalization (DG) aims to generalize a model trained on multiple source (i. e., training) domains to a distributionally different target (i. e., test) domain.
Mitigating Generation Shifts for Generalized Zero-Shot Learning
Generalized Zero-Shot Learning (GZSL) is the task of leveraging semantic information (e. g., attributes) to recognize the seen and unseen samples, where unseen classes are not observable during training.
CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation
Existing visually-aware models make use of the visual features as a separate collaborative signal similarly to other features to directly predict the user's preference without considering a potential bias, which gives rise to a visually biased recommendation.
Exploiting Positional Information for Session-based Recommendation
According to our analysis, existing positional encoding schemes are generally forward-aware only, which can hardly represent the dynamics of the intention in a session.
Exploiting Cross-Session Information for Session-based Recommendation with Graph Neural Networks
Different from the traditional recommender system, the session-based recommender system introduces the concept of the session, i. e., a sequence of interactions between a user and multiple items within a period, to preserve the user's recent interest.
Semantics Disentangling for Generalized Zero-Shot Learning
Generalized zero-shot learning (GZSL) aims to classify samples under the assumption that some classes are not observable during training.
GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation
On one hand, when a new session arrives, a session graph with a global attribute is constructed based on the current session and its associate user.
Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks
In this paper, therefore, we study the item transition pattern by constructing a session graph and propose a novel model which collaboratively considers the sequence order and the latent order in the session graph for a session-based recommender system.
