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Found 15 papers, 5 papers with code
A Mixed Learning Objective for Neural Machine Translation
At word-level, it calculates semantic similarity between predicted and ground truth words.
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.
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 #11 on
Conversational Response Selection
on E-commerce
