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Found 20 papers, 10 papers with code
Tencent submission for WMT20 Quality Estimation Shared Task
This paper presents Tencent’s submission to the WMT20 Quality Estimation (QE) Shared Task: Sentence-Level Post-editing Effort for English-Chinese in Task 2.
TempoGPT: Enhancing Temporal Reasoning via Quantizing Embedding
To address these challenges, we propose a multi-modal time series data construction approach and a multi-modal time series language model (TLM), TempoGPT.
Sensorformer: Cross-patch attention with global-patch compression is effective for high-dimensional multivariate time series forecasting
Among the existing Transformer-based multivariate time series forecasting methods, iTransformer, which treats each variable sequence as a token and only explicitly extracts cross-variable dependencies, and PatchTST, which adopts a channel-independent strategy and only explicitly extracts cross-time dependencies, both significantly outperform most Channel-Dependent Transformer that simultaneously extract cross-time and cross-variable dependencies.
Mitigating the Negative Impact of Over-association for Conversational Query Production
We carefully analyze the negative effects of this phenomenon on pretrained Seq2seq query producers and then propose effective instance-level weighting strategies for training to mitigate these issues from multiple perspectives.
MA-CDMR: An Intelligent Cross-domain Multicast Routing Method based on Multiagent Deep Reinforcement Learning in Multi-domain SDWN
An agent is established for each controller, and a cooperation mechanism between multiple agents is designed to effectively optimize cross-domain multicast routing and ensure consistency and validity in the representation of network state information for cross-domain multicast routing decisions.
A Deep Neural Network Framework for Solving Forward and Inverse Problems in Delay Differential Equations
We propose a unified framework for delay differential equations (DDEs) based on deep neural networks (DNNs) - the neural delay differential equations (NDDEs), aimed at solving the forward and inverse problems of delay differential equations.
Rethinking Multi-view Representation Learning via Distilled Disentangling
To this end, we propose an innovative framework for multi-view representation learning, which incorporates a technique we term 'distilled disentangling'.
Fine-tuning Large Language Models for Domain-specific Machine Translation
Large language models (LLMs) have shown great potential in domain-specific machine translation (MT).
BESTMVQA: A Benchmark Evaluation System for Medical Visual Question Answering
Medical Visual Question Answering (Med-VQA) is a very important task in healthcare industry, which answers a natural language question with a medical image.
Towards Better Multi-modal Keyphrase Generation via Visual Entity Enhancement and Multi-granularity Image Noise Filtering
2) the input text and image are often not perfectly matched, and thus the image may introduce noise into the model.
Disentangling Multi-view Representations Beyond Inductive Bias
In this paper, we propose a novel multi-view representation disentangling method that aims to go beyond inductive biases, ensuring both interpretability and generalizability of the resulting representations.
ConKI: Contrastive Knowledge Injection for Multimodal Sentiment Analysis
Multimodal Sentiment Analysis leverages multimodal signals to detect the sentiment of a speaker.
A Sequence-to-Sequence&Set Model for Text-to-Table Generation
Specifically, in addition to a text encoder encoding the input text, our model is equipped with a table header generator to first output a table header, i. e., the first row of the table, in the manner of sequence generation.
Search-Map-Search: A Frame Selection Paradigm for Action Recognition
To overcome the limitations of existing methods, we propose a Search-Map-Search learning paradigm which combines the advantages of heuristic search and supervised learning to select the best combination of frames from a video as one entity.
LA3: Efficient Label-Aware AutoAugment
Automated augmentation is an emerging and effective technique to search for data augmentation policies to improve generalizability of deep neural network training.
A Clustering-guided Contrastive Fusion for Multi-view Representation Learning
To this end, we utilize a deep fusion network to fuse view-specific representations into the view-common representation, extracting high-level semantics for obtaining robust representation.
WR-ONE2SET: Towards Well-Calibrated Keyphrase Generation
Keyphrase generation aims to automatically generate short phrases summarizing an input document.
OVQA: A Clinically Generated Visual Question Answering Dataset
Medical visual question answering (Med-VQA) is a challenging problem that aims to take a medical image and a clinical question about the image as input and output a correct answer in natural language.
Verdi: Quality Estimation and Error Detection for Bilingual Corpora
Verdi adopts two word predictors to enable diverse features to be extracted from a pair of sentences for subsequent quality estimation, including a transformer-based neural machine translation (NMT) model and a pre-trained cross-lingual language model (XLM).
Reinforced Curriculum Learning on Pre-trained Neural Machine Translation Models
Specifically, we propose a data selection framework based on Deterministic Actor-Critic, in which a critic network predicts the expected change of model performance due to a certain sample, while an actor network learns to select the best sample out of a random batch of samples presented to it.
