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Found 24 papers, 8 papers with code
Improving Constituent Representation with Hypertree Neural Networks
Many natural language processing tasks involve text spans and thus high-quality span representations are needed to enhance neural approaches to these tasks.
A Multi-Task Dual-Tree Network for Aspect Sentiment Triplet Extraction
Aspect Sentiment Triplet Extraction (ASTE) aims at extracting triplets from a given sentence, where each triplet includes an aspect, its sentiment polarity, and a corresponding opinion explaining the polarity.
A Mixed Learning Objective for Neural Machine Translation
At word-level, it calculates semantic similarity between predicted and ground truth words.
MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Network
The STcAM with fine-pruning uses one-dimensional convolution (Conv1D) to extract spatial features and subsequently utilizes the Bidirectional Long Short Term Memory (Bi-LSTM) to extract the temporal features, where the attention mechanism will focus on the important time steps.
Syntactic Ghost: An Imperceptible General-purpose Backdoor Attacks on Pre-trained Language Models
Specifically, the method hostilely manipulates poisoned samples with different predefined syntactic structures as stealth triggers and then implants the backdoor to pre-trained representation space without disturbing the primitive knowledge.
How Large Language Models Encode Context Knowledge? A Layer-Wise Probing Study
Previous work has showcased the intriguing capability of large language models (LLMs) in retrieving facts and processing context knowledge.
Acquiring Clean Language Models from Backdoor Poisoned Datasets by Downscaling Frequency Space
In this paper, we investigate the learning mechanisms of backdoor LMs in the frequency space by Fourier analysis.
Investigating Multi-Hop Factual Shortcuts in Knowledge Editing of Large Language Models
Recent work has showcased the powerful capability of large language models (LLMs) in recalling knowledge and reasoning.
Improving Non-autoregressive Machine Translation with Error Exposure and Consistency Regularization
Being one of the IR-NAT (Iterative-refinemennt-based NAT) frameworks, the Conditional Masked Language Model (CMLM) adopts the mask-predict paradigm to re-predict the masked low-confidence tokens.
An Enhanced Prompt-Based LLM Reasoning Scheme via Knowledge Graph-Integrated Collaboration
While Large Language Models (LLMs) demonstrate exceptional performance in a multitude of Natural Language Processing (NLP) tasks, they encounter challenges in practical applications, including issues with hallucinations, inadequate knowledge updating, and limited transparency in the reasoning process.
R-Judge: Benchmarking Safety Risk Awareness for LLM Agents
We introduce R-Judge, a benchmark crafted to evaluate the proficiency of LLMs in judging and identifying safety risks given agent interaction records.
Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents
Large language models (LLMs) have dramatically enhanced the field of language intelligence, as demonstrably evidenced by their formidable empirical performance across a spectrum of complex reasoning tasks.
UOR: Universal Backdoor Attacks on Pre-trained Language Models
In this paper, we first summarize the requirements that a more threatening backdoor attack against PLMs should satisfy, and then propose a new backdoor attack method called UOR, which breaks the bottleneck of the previous approach by turning manual selection into automatic optimization.
FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning
In this paper, we propose "FedPrompt" to study prompt tuning in a model split aggregation way using FL, and prove that split aggregation greatly reduces the communication cost, only 0. 01% of the PLMs' parameters, with little decrease on accuracy both on IID and Non-IID data distribution.
Few-Shot Table-to-Text Generation with Prefix-Controlled Generator
To alleviate these problems, we propose a prompt-based approach, Prefix-Controlled Generator (i. e., PCG), for few-shot table-to-text generation.
ASAP-Net: Attention and Structure Aware Point Cloud Sequence Segmentation
In this paper, we further improve spatio-temporal point cloud feature learning with a flexible module called ASAP considering both attention and structure information across frames, which we find as two important factors for successful segmentation in dynamic point clouds.
Hierarchy-Aware Global Model for Hierarchical Text Classification
Hierarchical text classification is an essential yet challenging subtask of multi-label text classification with a taxonomic hierarchy.
MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding
Online Real-Time Bidding (RTB) is a complex auction game among which advertisers struggle to bid for ad impressions when a user request occurs.
Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation
User modeling is an essential task for online rec- ommender systems.
Ranked #2 on
Recommendation Systems
on Amazon Product Data
A Unified Syntax-aware Framework for Semantic Role Labeling
Semantic role labeling (SRL) aims to recognize the predicate-argument structure of a sentence.
Multiple Character Embeddings for Chinese Word Segmentation
Chinese word segmentation (CWS) is often regarded as a character-based sequence labeling task in most current works which have achieved great success with the help of powerful neural networks.
Sliced Recurrent Neural Networks
In this paper, we introduce sliced recurrent neural networks (SRNNs), which could be parallelized by slicing the sequences into many subsequences.
Ranked #6 on
Sentiment Analysis
on Amazon Review Full
Modeling Multi-turn Conversation with Deep Utterance Aggregation
In this paper, we formulate previous utterances into context using a proposed deep utterance aggregation model to form a fine-grained context representation.
Ranked #13 on
Conversational Response Selection
on E-commerce
