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Found 42 papers, 24 papers with code
Self Question-answering: Aspect-based Sentiment Analysis by Role Flipped Machine Reading Comprehension
In this paper, we investigate the unified ABSA task from the perspective of Machine Reading Comprehension (MRC) by observing that the aspect and the opinion terms can serve as the query and answer in MRC interchangeably.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
Fact-Preserved Personalized News Headline Generation
In this paper, we propose a framework Fact-Preserved Personalized News Headline Generation (short for FPG), to prompt a tradeoff between personalization and consistency.
Panoramic Interests: Stylistic-Content Aware Personalized Headline Generation
Prevailing methods focus on user-oriented content preferences, but most of them overlook the fact that diverse stylistic preferences are integral to users' panoramic interests, leading to suboptimal personalization.
Controlling Large Language Models Through Concept Activation Vectors
During inference, GCAV steers the concept vector in LLMs, for example, by removing the toxicity concept vector from the activation layers.
Financial Risk Assessment via Long-term Payment Behavior Sequence Folding
We evaluate LBSF on the financial risk assessment task using a large-scale real-world dataset.
Adapting Vision-Language Models to Open Classes via Test-Time Prompt Tuning
Adapting pre-trained models to open classes is a challenging problem in machine learning.
AlphaForge: A Framework to Mine and Dynamically Combine Formulaic Alpha Factors
The complexity of financial data, characterized by its variability and low signal-to-noise ratio, necessitates advanced methods in quantitative investment that prioritize both performance and interpretability. Transitioning from early manual extraction to genetic programming, the most advanced approach in the alpha factor mining domain currently employs reinforcement learning to mine a set of combination factors with fixed weights.
LOGIN: A Large Language Model Consulted Graph Neural Network Training Framework
In this paper, we aim to streamline the GNN design process and leverage the advantages of Large Language Models (LLMs) to improve the performance of GNNs on downstream tasks.
EFSA: Towards Event-Level Financial Sentiment Analysis
In this paper, we extend financial sentiment analysis~(FSA) to event-level since events usually serve as the subject of the sentiment in financial text.
Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning
In practice, a set of formulaic alphas is often used together for better modeling precision, so we need to find synergistic formulaic alpha sets that work well together.
Along the Time: Timeline-traced Embedding for Temporal Knowledge Graph Completion
In order to represent the facts happening in a specific time, temporal knowledge graph (TKG) embedding approaches are put forward.
Ranked #2 on
Link Prediction
on GDELT
Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN
However, both strategies are faced with some immediate problems: raw features cannot represent various properties of nodes (e. g., structure information), and representations learned by supervised GNN may suffer from the poor performance of the classifier on the poisoned graph.
User-Centric Conversational Recommendation with Multi-Aspect User Modeling
In this work, we highlight that the user's historical dialogue sessions and look-alike users are essential sources of user preferences besides the current dialogue session in CRS.
Ranked #3 on
Recommendation Systems
on ReDial
(Recall@50 metric)
Multi-view Multi-behavior Contrastive Learning in Recommendation
We argue that MBR models should: (1) model the coarse-grained commonalities between different behaviors of a user, (2) consider both individual sequence view and global graph view in multi-behavior modeling, and (3) capture the fine-grained differences between multiple behaviors of a user.
Mind the Gap: Cross-Lingual Information Retrieval with Hierarchical Knowledge Enhancement
The proposed model encodes the textual information in queries, documents and the KG with multilingual BERT, and incorporates the KG information in the query-document matching process with a hierarchical information fusion mechanism.
Rethinking Pareto Approaches in Constrained Reinforcement Learning
Constrained Reinforcement Learning (CRL) burgeons broad interest in recent years, which pursues both goals of maximizing long-term returns and constraining costs.
ConRPG: Paraphrase Generation using Contexts as Regularizer
A long-standing issue with paraphrase generation is how to obtain reliable supervision signals.
Layer-wise Model Pruning based on Mutual Information
The proposed pruning strategy offers merits over weight-based pruning techniques: (1) it avoids irregular memory access since representations and matrices can be squeezed into their smaller but dense counterparts, leading to greater speedup; (2) in a manner of top-down pruning, the proposed method operates from a more global perspective based on training signals in the top layer, and prunes each layer by propagating the effect of global signals through layers, leading to better performances at the same sparsity level.
Follow the Prophet: Accurate Online Conversion Rate Prediction in the Face of Delayed Feedback
The delayed feedback problem is one of the imperative challenges in online advertising, which is caused by the highly diversified feedback delay of a conversion varying from a few minutes to several days.
PENS: A Dataset and Generic Framework for Personalized News Headline Generation
To build up a benchmark for this problem, we publicize a large-scale dataset named PENS (PErsonalized News headlineS).
GuideBoot: Guided Bootstrap for Deep Contextual Bandits
The proposed method is efficient as it can make decisions on-the-fly by utilizing only one randomly chosen model, but is also effective as we show that it can be viewed as a non-Bayesian approximation of Thompson sampling.
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information
Recent pretraining models in Chinese neglect two important aspects specific to the Chinese language: glyph and pinyin, which carry significant syntax and semantic information for language understanding.
Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment Classification
In this paper, we propose a novel iterative network pruning with uncertainty regularization method for lifelong sentiment classification (IPRLS), which leverages the principles of network pruning and weight regularization.
Defending Against Backdoor Attacks in Natural Language Generation
The frustratingly fragile nature of neural network models make current natural language generation (NLG) systems prone to backdoor attacks and generate malicious sequences that could be sexist or offensive.
Imagine, Reason and Write: Visual Storytelling with Graph Knowledge and Relational Reasoning
Finally, we integrate the imaginary concepts and relational knowledge to generate human-like story based on the original semantics of images.
Ranked #2 on
Visual Storytelling
on VIST
Sentence Similarity Based on Contexts
The proposed framework is based on the core idea that the meaning of a sentence should be defined by its contexts, and that sentence similarity can be measured by comparing the probabilities of generating two sentences given the same context.
Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection
Graph-based fraud detection approaches have escalated lots of attention recently due to the abundant relational information of graph-structured data, which may be beneficial for the detection of fraudsters.
Ranked #5 on
Fraud Detection
on Amazon-Fraud
Interactive Key-Value Memory-augmented Attention for Image Paragraph Captioning
In this paper, we propose an Interactive key-value Memory- augmented Attention model for image Paragraph captioning (IMAP) to keep track of the attention history (salient objects coverage information) along with the update-chain of the decoder state and therefore avoid generating repetitive or incomplete image descriptions.
Meet Changes with Constancy: Learning Invariance in Multi-Source Translation
Multi-source neural machine translation aims to translate from parallel sources of information (e. g. languages, images, etc.)
Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information
Named entity recognition (NER) is highly sensitive to sentential syntactic and semantic properties where entities may be extracted according to how they are used and placed in the running text.
Ranked #3 on
Named Entity Recognition (NER)
on WNUT 2016
Chinese Named Entity Recognition
named-entity-recognition
+2
Improving Robustness and Generality of NLP Models Using Disentangled Representations
We show that models trained with the proposed criteria provide better robustness and domain adaptation ability in a wide range of supervised learning tasks.
Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-way Attentions of Auto-analyzed Knowledge
Chinese word segmentation (CWS) and part-of-speech (POS) tagging are important fundamental tasks for Chinese language processing, where joint learning of them is an effective one-step solution for both tasks.
Discovering Protagonist of Sentiment with Aspect Reconstructed Capsule Network
In this paper, we aim to improve ATSA by discovering the potential aspect terms of the predicted sentiment polarity when the aspect terms of a test sentence are unknown.
A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) has attracted increasing attention recently due to its broad applications.
Ranked #4 on
Aspect-Based Sentiment Analysis (ABSA)
on MAMS
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Reading Like HER: Human Reading Inspired Extractive Summarization
In this work, we re-examine the problem of extractive text summarization for long documents.
Ranked #8 on
Extractive Text Summarization
on CNN / Daily Mail
Unsupervised Neural Aspect Extraction with Sememes
Aspect extraction relies on identifying aspects by discovering coherence among words, which is challenging when word meanings are diversified and processing on short texts.
Aspect Extraction
Aspect Term Extraction and Sentiment Classification
+1
Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions
It is often observed that the probabilistic predictions given by a machine learning model can disagree with averaged actual outcomes on specific subsets of data, which is also known as the issue of miscalibration.
Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings
We propose Meta-Embedding, a meta-learning-based approach that learns to generate desirable initial embeddings for new ad IDs.
Hierarchical Neural Network for Extracting Knowledgeable Snippets and Documents
Additionally, a "low-level sharing, high-level splitting" structure of CNN is designed to handle the documents from different content domains.
Free-rider Episode Screening via Dual Partition Model
An effective technique for filtering free-rider episodes is using a partition model to divide an episode into two consecutive subepisodes and comparing the observed support of such episode with its expected support under the assumption that these two subepisodes occur independently.
