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Found 46 papers, 28 papers with code
Maximal Clique Based Non-Autoregressive Open Information Extraction
However, the popular OpenIE systems usually output facts sequentially in the way of predicting the next fact conditioned on the previous decoded ones, which enforce an unnecessary order on the facts and involve the error accumulation between autoregressive steps.
Enhancing Chinese Pre-trained Language Model via Heterogeneous Linguistics Graph
Chinese pre-trained language models usually exploit contextual character information to learn representations, while ignoring the linguistics knowledge, e. g., word and sentence information.
Mixture of Hidden-Dimensions Transformer
MOHD offers a new perspective for scaling the model, showcasing the potential of hidden dimension sparsity to boost efficiency
MoR: Mixture of Ranks for Low-Rank Adaptation Tuning
Low-Rank Adaptation (LoRA) drives research to align its performance with full fine-tuning.
Revealing the Challenge of Detecting Character Knowledge Errors in LLM Role-Playing
Large language model (LLM) role-playing has gained widespread attention, where the authentic character knowledge is crucial for constructing realistic LLM role-playing agents.
NACL: A General and Effective KV Cache Eviction Framework for LLMs at Inference Time
Large Language Models (LLMs) have ignited an innovative surge of AI applications, marking a new era of exciting possibilities equipped with extended context windows.
LoginMEA: Local-to-Global Interaction Network for Multi-modal Entity Alignment
Other studies refine each uni-modal information with graph structures, but may introduce unnecessary relations in specific modalities.
IBMEA: Exploring Variational Information Bottleneck for Multi-modal Entity Alignment
To this end, we explore variational information bottleneck for multi-modal entity alignment (IBMEA), which emphasizes the alignment-relevant information and suppresses the alignment-irrelevant information in generating entity representations.
Optimal Transport Guided Correlation Assignment for Multimodal Entity Linking
Thereby, we exploit the correlation between multimodal features to enhance multimodal fusion, and the correlation between mentions and entities to enhance fine-grained matching.
DHA: Learning Decoupled-Head Attention from Transformer Checkpoints via Adaptive Heads Fusion
Large language models (LLMs) with billions of parameters demonstrate impressive performance.
CDRNP: Cross-Domain Recommendation to Cold-Start Users via Neural Process
Cross-domain recommendation (CDR) has been proven as a promising way to tackle the user cold-start problem, which aims to make recommendations for users in the target domain by transferring the user preference derived from the source domain.
Adaptive Data Augmentation for Aspect Sentiment Quad Prediction
Aspect sentiment quad prediction (ASQP) aims to predict the quad sentiment elements for a given sentence, which is a critical task in the field of aspect-based sentiment analysis.
FFT: Towards Harmlessness Evaluation and Analysis for LLMs with Factuality, Fairness, Toxicity
The widespread of generative artificial intelligence has heightened concerns about the potential harms posed by AI-generated texts, primarily stemming from factoid, unfair, and toxic content.
Prompt2Gaussia: Uncertain Prompt-learning for Script Event Prediction
In this paper, we regard public pre-trained language models as knowledge bases and automatically mine the script-related knowledge via prompt-learning.
Wider and Deeper LLM Networks are Fairer LLM Evaluators
Each perspective corresponds to the role of a specific LLM neuron in the first layer.
Universal Information Extraction with Meta-Pretrained Self-Retrieval
Inspired by the fact that large amount of knowledge are stored in the pretrained language models~(PLM) and can be retrieved explicitly, in this paper, we propose MetaRetriever to retrieve task-specific knowledge from PLMs to enhance universal IE.
ID-MixGCL: Identity Mixup for Graph Contrastive Learning
Graph contrastive learning (GCL) has recently achieved substantial advancements.
Contrastive Cross-Domain Sequential Recommendation
Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains.
Enhancing Multimodal Entity and Relation Extraction with Variational Information Bottleneck
For the first issue, a refinement-regularizer probes the information-bottleneck principle to balance the predictive evidence and noisy information, yielding expressive representations for prediction.
Event Causality Extraction with Event Argument Correlations
Event Causality Identification (ECI), which aims to detect whether a causality relation exists between two given textual events, is an important task for event causality understanding.
Enhancing Joint Multiple Intent Detection and Slot Filling with Global Intent-Slot Co-occurrence
To be specific, an intent-slot co-occurrence graph is constructed based on the entire training corpus to globally discover correlation between intents and slots.
Ranked #7 on
Slot Filling
on MixATIS
Towards Generalized Open Information Extraction
Open Information Extraction (OpenIE) facilitates the open-domain discovery of textual facts.
Layout-Aware Information Extraction for Document-Grounded Dialogue: Dataset, Method and Demonstration
In this paper, we propose a Layout-aware document-level Information Extraction dataset, LIE, to facilitate the study of extracting both structural and semantic knowledge from visually rich documents (VRDs), so as to generate accurate responses in dialogue systems.
Relation-Guided Few-Shot Relational Triple Extraction
To instantiate this strategy, we further propose a model, RelATE, which builds a dual-level attention to aggregate relationrelevant information to detect the relation occurrence and utilizes the annotated samples of the detected relations to extract the corresponding head/tail entities.
Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck
As a promising way, Cross-Domain Recommendation (CDR) has attracted a surge of interest, which aims to transfer the user preferences observed in the source domain to make recommendations in the target domain.
Document-Level Event Extraction via Human-Like Reading Process
Meanwhile, rough reading is explored in a multi-round manner to discover undetected events, thus the multi-events problem is handled.
Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising Learning
Distantly supervised named entity recognition (DS-NER) efficiently reduces labor costs but meanwhile intrinsically suffers from the label noise due to the strong assumption of distant supervision.
Deep Structural Point Process for Learning Temporal Interaction Networks
A temporal interaction network consists of a series of chronological interactions between users and items.
CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction
Event extraction (EE) is a crucial information extraction task that aims to extract event information in texts.
Discontinuous Named Entity Recognition as Maximal Clique Discovery
Named entity recognition (NER) remains challenging when entity mentions can be discontinuous.
FITAnnotator: A Flexible and Intelligent Text Annotation System
In this paper, we introduce FITAnnotator, a generic web-based tool for efficient text annotation.
Bipartite Graph Embedding via Mutual Information Maximization
However, the global properties of bipartite graph, including community structures of homogeneous nodes and long-range dependencies of heterogeneous nodes, are not well preserved.
Few-Shot Event Detection with Prototypical Amortized Conditional Random Field
Event detection tends to struggle when it needs to recognize novel event types with a few samples.
Label Enhanced Event Detection with Heterogeneous Graph Attention Networks
A heterogeneous graph attention networks is then introduced to propagate relational message and enrich information interaction.
Learning to Prune Dependency Trees with Rethinking for Neural Relation Extraction
Dependency trees have been shown to be effective in capturing long-range relations between target entities.
Porous Lattice Transformer Encoder for Chinese NER
Incorporating lexicons into character-level Chinese NER by lattices is proven effective to exploitrich word boundary information.
Document-level Relation Extraction with Dual-tier Heterogeneous Graph
Document-level relation extraction (RE) poses new challenges over its sentence-level counterpart since it requires an adequate comprehension of the whole document and the multi-hop reasoning ability across multiple sentences to reach the final result.
TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking
To mitigate the issue, we propose in this paper a one-stage joint extraction model, namely, TPLinker, which is capable of discovering overlapping relations sharing one or both entities while immune from the exposure bias.
Ranked #2 on
Relation Extraction
on NYT11-HRL
Adaptive Attentional Network for Few-Shot Knowledge Graph Completion
Recent attempts solve this problem by learning static representations of entities and references, ignoring their dynamic properties, i. e., entities may exhibit diverse roles within task relations, and references may make different contributions to queries.
Coarse-to-Fine Pre-training for Named Entity Recognition
More recently, Named Entity Recognition hasachieved great advances aided by pre-trainingapproaches such as BERT.
Inductive Unsupervised Domain Adaptation for Few-Shot Classification via Clustering
We first build a representation extractor to derive features for unlabeled data from the target domain (no test data is necessary) and then group them with a cluster miner.
Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation
Previous studies on the task have verified the effectiveness of integrating syntactic dependency into graph convolutional networks.
Enhancing Pre-trained Chinese Character Representation with Word-aligned Attention
Most Chinese pre-trained models take character as the basic unit and learn representation according to character's external contexts, ignoring the semantics expressed in the word, which is the smallest meaningful utterance in Chinese.
Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy
Joint extraction of entities and relations aims to detect entity pairs along with their relations using a single model.
Ranked #1 on
Relation Extraction
on NYT-single
Beyond Word Attention: Using Segment Attention in Neural Relation Extraction
Relation extraction studies the issue of predicting semantic relations between pairs of entities in sentences.
Ranked #30 on
Relation Extraction
on TACRED
