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Found 33 papers, 17 papers with code
Analytical Reasoning of Text
In this paper, we study the challenge of analytical reasoning of text and collect a new dataset consisting of questions from the Law School Admission Test from 1991 to 2016.
UECA-Prompt: Universal Prompt for Emotion Cause Analysis
To address the first two problems, this paper proposes a universal prompt tuning method to solve different ECA tasks in the unified framework.
Ranked #1 on
Emotion-Cause Pair Extraction
on ECPE
A Hierarchical Framework with Spatio-Temporal Consistency Learning for Emergence Detection in Complex Adaptive Systems
Representations of the agents and the system are learned by preserving the intrinsic spatio-temporal consistency in a self-supervised manner.
Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement
Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respective subproblems, thus a limited Pareto set.
Democratizing Reasoning Ability: Tailored Learning from Large Language Model
In this paper, we propose a tailored learning approach to distill such reasoning ability to smaller LMs to facilitate the democratization of the exclusive reasoning ability.
Adaptive-Solver Framework for Dynamic Strategy Selection in Large Language Model Reasoning
Experimental results from complex reasoning tasks reveal that the prompting method adaptation and decomposition granularity adaptation enhance performance across all tasks.
Solving Dynamic Traveling Salesman Problems With Deep Reinforcement Learning
This brings in a dynamic version of the traveling salesman problem (DTSP), which takes into account the information of real-time traffic and customer requests.
Disentangling Reasoning Capabilities from Language Models with Compositional Reasoning Transformers
This paper presents ReasonFormer, a unified reasoning framework for mirroring the modular and compositional reasoning process of humans in complex decision-making.
Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation
Recent neural methods for vehicle routing problems always train and test the deep models on the same instance distribution (i. e., uniform).
Improving Task Generalization via Unified Schema Prompt
Task generalization has been a long standing challenge in Natural Language Processing (NLP).
ProQA: Structural Prompt-based Pre-training for Unified Question Answering
Furthermore, ProQA exhibits strong ability in both continual learning and transfer learning by taking the advantages of the structural prompt.
Graph Neural Networks with Dynamic and Static Representations for Social Recommendation
GNN-DSR models the short-term dynamic and long-term static interactional representations of the user's interest and the item's attraction, respectively.
Reasoning over Hybrid Chain for Table-and-Text Open Domain QA
CARP utilizes hybrid chain to model the explicit intermediate reasoning process across table and text for question answering.
Ranked #2 on
Question Answering
on OTT-QA
MODRL/D-EL: Multiobjective Deep Reinforcement Learning with Evolutionary Learning for Multiobjective Optimization
The evolutionary learning is also employed to fine-tune the parameters of the models.
Topic-to-Essay Generation with Comprehensive Knowledge Enhancement
For internal knowledge enhancement, both topics and related essays are fed to a teacher network as source information.
Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization Problems
When these problems are extended to multiobjective ones, it becomes difficult for the existing DRL approaches to flexibly and efficiently deal with multiple subproblems determined by weight decomposition of objectives.
AR-LSAT: Investigating Analytical Reasoning of Text
Analytical reasoning is an essential and challenging task that requires a system to analyze a scenario involving a set of particular circumstances and perform reasoning over it to make conclusions.
Neural Relational Inference with Efficient Message Passing Mechanisms
Additionally, the structural prior knowledge, symmetry as a special case, is introduced for better relation prediction in more complex systems.
Multi-choice Relational Reasoning for Machine Reading Comprehension
We propose a multi-choice relational reasoning (McR$^2$) model with an aim to enable relational reasoning on candidates based on fusion representations of document, query and candidates.
Neural Deepfake Detection with Factual Structure of Text
To address this, we propose a graph-based model that utilizes the factual structure of a document for deepfake detection of text.
LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network
The graph is used to obtain graph-enhanced contextual representations of words in Transformer-based architecture.
A Heterogeneous Graph with Factual, Temporal and Logical Knowledge for Question Answering Over Dynamic Contexts
We study question answering over a dynamic textual environment.
A Novel Framework with Information Fusion and Neighborhood Enhancement for User Identity Linkage
The information fusion component adopts a group of encoders and decoders to fuse heterogeneous information and generate discriminative node embeddings for preliminary matching.
MODRL/D-AM: Multiobjective Deep Reinforcement Learning Algorithm Using Decomposition and Attention Model for Multiobjective Optimization
Each subproblem is modeled with a pointer network and the model is trained with reinforcement learning.
Utilizing BERT Intermediate Layers for Aspect Based Sentiment Analysis and Natural Language Inference
Aspect based sentiment analysis aims to identify the sentimental tendency towards a given aspect in text.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing Problems
However, the fact is, the state of an instance is changed according to the decision that the model made at different construction steps, and the node features should be updated correspondingly.
CatGAN: Category-aware Generative Adversarial Networks with Hierarchical Evolutionary Learning for Category Text Generation
This paper proposes a category-aware GAN (CatGAN) which consists of an efficient category-aware model for category text generation and a hierarchical evolutionary learning algorithm for training our model.
Reasoning Over Semantic-Level Graph for Fact Checking
We evaluate our system on FEVER, a benchmark dataset for fact checking, and find that rich structural information is helpful and both our graph-based mechanisms improve the accuracy.
Ranked #2 on
Fact Verification
on FEVER
Collective Mobile Sequential Recommendation: A Recommender System for Multiple Taxicabs
Mobile sequential recommendation was originally designed to find a promising route for a single taxicab.
Attentional Encoder Network for Targeted Sentiment Classification
Most of the previous approaches model context and target words with RNN and attention.
Ranked #1 on
Sentiment Analysis
on Twitter
Improving Question Answering by Commonsense-Based Pre-Training
Although neural network approaches achieve remarkable success on a variety of NLP tasks, many of them struggle to answer questions that require commonsense knowledge.
adaptive direction information in differential evolution for numerical optimization
In most of the DE algorithms, the neighborhood and direction information are not fully and simultaneously exploited to guide the search.
