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Found 25 papers, 9 papers with code
Deep Inductive Logic Reasoning for Multi-Hop Reading Comprehension
Multi-hop reading comprehension requires an ability to reason across multiple documents.
Deep Weighted MaxSAT for Aspect-based Opinion Extraction
Though deep learning has achieved significant success in various NLP tasks, most deep learning models lack the capability of encoding explicit domain knowledge to model complex causal relationships among different types of variables.
Variational Deep Logic Network for Joint Inference of Entities and Relations
Various deep neural networks have been proposed to jointly perform entity extraction and relation prediction, which only propagate information implicitly via representation learning.
Decomposing Label Space, Format and Discrimination: Rethinking How LLMs Respond and Solve Tasks via In-Context Learning
Counter-intuitively, we find that the demonstrations have a marginal impact on provoking discriminative knowledge of language models.
Re2LLM: Reflective Reinforcement Large Language Model for Session-based Recommendation
However, the former methods struggle with optimal prompts to elicit the correct reasoning of LLMs due to the lack of task-specific feedback, leading to unsatisfactory recommendations.
Can Language Models Act as Knowledge Bases at Scale?
Large language models (LLMs) have demonstrated remarkable proficiency in understanding and generating responses to complex queries through large-scale pre-training.
Backdoor Attacks on Dense Passage Retrievers for Disseminating Misinformation
To achieve this, we propose a perilous backdoor attack triggered by grammar errors in dense passage retrieval.
TELLER: A Trustworthy Framework for Explainable, Generalizable and Controllable Fake News Detection
The proliferation of fake news has emerged as a severe societal problem, raising significant interest from industry and academia.
Training Language Models to Generate Text with Citations via Fine-grained Rewards
While recent Large Language Models (LLMs) have proven useful in answering user queries, they are prone to hallucination, and their responses often lack credibility due to missing references to reliable sources.
Are Machines Better at Complex Reasoning? Unveiling Human-Machine Inference Gaps in Entailment Verification
Finally, we use this model to filter out inconsistent model-generated rationales in self-consistency decoding, resulting in a 6% accuracy improvement on average across three MCQ datasets.
Robust Domain Misinformation Detection via Multi-modal Feature Alignment
We also propose a framework that simultaneously considers application scenarios of domain generalization (in which the target domain data is unavailable) and domain adaptation (in which unlabeled target domain data is available).
Adapt in Contexts: Retrieval-Augmented Domain Adaptation via In-Context Learning
Large language models (LLMs) have showcased their capability with few-shot inference known as in-context learning.
Interpretable Multimodal Misinformation Detection with Logic Reasoning
Multimodal misinformation on online social platforms is becoming a critical concern due to increasing credibility and easier dissemination brought by multimedia content, compared to traditional text-only information.
Vera: A General-Purpose Plausibility Estimation Model for Commonsense Statements
Despite the much discussed capabilities of today's language models, they are still prone to silly and unexpected commonsense failures.
Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement
In this paper, we propose a novel hierarchical framework for sarcasm detection by exploring both the atomic-level congruity based on multi-head cross attention mechanism and the composition-level congruity based on graph neural networks, where a post with low congruity can be identified as sarcasm.
Elaboration-Generating Commonsense Question Answering at Scale
In question answering requiring common sense, language models (e. g., GPT-3) have been used to generate text expressing background knowledge that helps improve performance.
Domain Confused Contrastive Learning for Unsupervised Domain Adaptation
In this work, we study Unsupervised Domain Adaptation (UDA) in a challenging self-supervised approach.
Weakly-supervised Domain Adaption for Aspect Extraction via Multi-level Interaction Transfer
Specifically, the aspect category information is used to construct pivot knowledge for transfer with assumption that the interactions between sentence-level aspect category and token-level aspect terms are invariant across domains.
Integrating Deep Learning with Logic Fusion for Information Extraction
To combine such logic reasoning capabilities with learning capabilities of deep neural networks, we propose to integrate logical knowledge in the form of first-order logic into a deep learning system, which can be trained jointly in an end-to-end manner.
Syntactically Meaningful and Transferable Recursive Neural Networks for Aspect and Opinion Extraction
In this article, we explore the constructions of recursive neural networks based on the dependency tree of each sentence for associating syntactic structure with feature learning.
MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization
However, these methods only heuristically make training-based quantization applicable, without further analysis on how the approximated gradients can assist training of a quantized network.
Recursive Neural Structural Correspondence Network for Cross-domain Aspect and Opinion Co-Extraction
Fine-grained opinion analysis aims to extract aspect and opinion terms from each sentence for opinion summarization.
Coupled Multi-Layer Attentions for Co-Extraction of Aspect and Opinion Terms
To achieve this task, one effective approach is to exploit relations between aspect terms and opinion terms by parsing syntactic structure for each sentence.
Multi-task memory networks for category-specific aspect and opinion terms co-extraction
This problem involves the identification of aspect and opinion terms within each sentence, as well as the categorization of the identified terms.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis
Previous studies have shown that exploiting connections between aspect and opinion terms is promising for this task.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
