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Found 14 papers, 7 papers with code
How Vital is the Jurisprudential Relevance: Law Article Intervened Legal Case Retrieval and Matching
Legal case retrieval (LCR) aims to automatically scour for comparable legal cases based on a given query, which is crucial for offering relevant precedents to support the judgment in intelligent legal systems.
Distinguish Confusion in Legal Judgment Prediction via Revised Relation Knowledge
On the other hand, D-LADAN presents a novel momentum-updated memory mechanism to dynamically sense the posterior similarity between law articles (or charges) and a weighted GDO to adaptively capture the distinctions for revising the inductive bias caused by the data imbalance problem.
Representation Learning of Tangled Key-Value Sequence Data for Early Classification
To address this problem, we propose a novel method, i. e., Key-Value sequence Early Co-classification (KVEC), which leverages both inner- and inter-correlations of items in a tangled key-value sequence through key correlation and value correlation to learn a better sequence representation.
Robust Visual Question Answering: Datasets, Methods, and Future Challenges
Specifically, we first provide an overview of the development process of datasets from in-distribution and out-of-distribution perspectives.
The Knowledge Alignment Problem: Bridging Human and External Knowledge for Large Language Models
Large language models often necessitate grounding on external knowledge to generate faithful and reliable answers.
Multi-Action Dialog Policy Learning from Logged User Feedback
To cope with this problem, we explore to improve multi-action dialog policy learning with explicit and implicit turn-level user feedback received for historical predictions (i. e., logged user feedback) that are cost-efficient to collect and faithful to real-world scenarios.
Fast Gumbel-Max Sketch and its Applications
The well-known Gumbel-Max Trick for sampling elements from a categorical distribution (or more generally a non-negative vector) and its variants have been widely used in areas such as machine learning and information retrieval.
Federated Learning over Coupled Graphs
In this work, we propose a novel FL framework for graph data, FedCog, to efficiently handle coupled graphs that are a kind of distributed graph data, but widely exist in a variety of real-world applications such as mobile carriers' communication networks and banks' transaction networks.
"Think Before You Speak": Improving Multi-Action Dialog Policy by Planning Single-Action Dialogs
Multi-action dialog policy (MADP), which generates multiple atomic dialog actions per turn, has been widely applied in task-oriented dialog systems to provide expressive and efficient system responses.
Learning to Check Contract Inconsistencies
This will result in the issue of contract inconsistencies, which may severely impair the legal validity of the contract.
Distinguish Confusing Law Articles for Legal Judgment Prediction
Legal Judgment Prediction (LJP) is the task of automatically predicting a law case's judgment results given a text describing its facts, which has excellent prospects in judicial assistance systems and convenient services for the public.
Fast Generating A Large Number of Gumbel-Max Variables
Instead of computing $k$ independent Gumbel random variables directly, we find that there exists a technique to generate these variables in descending order.
MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions
To resolve these problems, we present MR-GNN, an end-to-end graph neural network with the following features: i) it uses a multi-resolution based architecture to extract node features from different neighborhoods of each node, and, ii) it uses dual graph-state long short-term memory networks (L-STMs) to summarize local features of each graph and extracts the interaction features between pairwise graphs.
On Analyzing Estimation Errors due to Constrained Connections in Online Review Systems
Constrained connection is the phenomenon that a reviewer can only review a subset of products/services due to narrow range of interests or limited attention capacity.
