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Found 31 papers, 7 papers with code
Synchronous Dual Network with Cross-Type Attention for Joint Entity and Relation Extraction
In this paper, we design a novel synchronous dual network (SDN) with cross-type attention via separately and interactively considering the entity types and relation types.
Adversarial Soft Prompt Tuning for Cross-Domain Sentiment Analysis
On the other hand, AdSPT uses a novel domain adversarial training strategy to learn domain-invariant representations between each source domain and the target domain.
XMU’s Simultaneous Translation System at NAACL 2021
This paper describes our two systems submitted to the simultaneous translation evaluation at the 2nd automatic simultaneous translation workshop.
一种基于IDLSTM+CRF的中文主地域抽取方法(A Chinese Main Location Extraction Method based on IDLSTM+CRF)
“新闻文本通常会涉及多个地域, 主地域则描述了文本舆情内容的地域属性, 是进行舆情分析的关键属性。目前深度学习领域针对主地域自动抽取的研究还比较少。基于此, 本文构建了一个基于IDLSTM+CRF的主地域抽取系统。该系统通过地名识别、主地域抽取、主地域补全三大模块实现对主地域标签的自动抽取和补全。在公开数据集上的实验结果表明, 我们的方法在地名识别任务上要优于BiLSTM+CRF等模型。而对于主地域抽取任务, 目前还没有标准的中文主地域评测集合。针对该问题, 我们标注并开源了1226条验证集和1500条测试集。最终, 我们的主地域抽取系统在两个集合上分别取得了91. 7%和84. 8%的抽取准确率, 并成功运用于线上生产环境。”
MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization
Scientific data visualization plays a crucial role in research by enabling the direct display of complex information and assisting researchers in identifying implicit patterns.
UniMem: Towards a Unified View of Long-Context Large Language Models
Although there exist various methods devoted to enhancing the long-context processing ability of large language models (LLMs), they are developed in an isolated manner and lack systematic analysis and integration of their strengths, hindering further developments.
How many preprints have actually been printed and why: a case study of computer science preprints on arXiv
In the case of these manuscripts, the traditional fuzzy matching method is incapable of mapping the preprint to the final published version.
Layer-wise Representation Fusion for Compositional Generalization
Inspired by this, we propose LRF, a novel \textbf{L}ayer-wise \textbf{R}epresentation \textbf{F}usion framework for CG, which learns to fuse previous layers' information back into the encoding and decoding process effectively through introducing a \emph{fuse-attention module} at each encoder and decoder layer.
Learning to Compose Representations of Different Encoder Layers towards Improving Compositional Generalization
There is mounting evidence that one of the reasons hindering CG is the representation of the encoder uppermost layer is entangled, i. e., the syntactic and semantic representations of sequences are entangled.
LEAPT: Learning Adaptive Prefix-to-prefix Translation For Simultaneous Machine Translation
Simultaneous machine translation, which aims at a real-time translation, is useful in many live scenarios but very challenging due to the trade-off between accuracy and latency.
Adapting Offline Speech Translation Models for Streaming with Future-Aware Distillation and Inference
A popular approach to streaming speech translation is to employ a single offline model with a wait-k policy to support different latency requirements, which is simpler than training multiple online models with different latency constraints.
MOPRD: A multidisciplinary open peer review dataset
MOPRD is a strong endorsement for further studies in peer review-related research and other applications.
Towards Better Document-level Relation Extraction via Iterative Inference
Existing methods usually directly predict the relations of all entity pairs of input document in a one-pass manner, ignoring the fact that predictions of some entity pairs heavily depend on the predicted results of other pairs.
Detecting and analyzing missing citations to published scientific entities
In the 12, 278 papers collected, 475 published scientific entities of computer science and mathematics are found to have missing citations.
Automatic Analysis of Available Source Code of Top Artificial Intelligence Conference Papers
Some organizations and researchers manually collect AI papers with available source code to contribute to the AI community.
A Token-level Contrastive Framework for Sign Language Translation
Sign Language Translation (SLT) is a promising technology to bridge the communication gap between the deaf and the hearing people.
Ranked #6 on
Sign Language Translation
on CSL-Daily
Automated scholarly paper review: Concepts, technologies, and challenges
Peer review is a widely accepted mechanism for research evaluation, playing a pivotal role in academic publishing.
A Document-Level Neural Machine Translation Model with Dynamic Caching Guided by Theme-Rheme Information
Research on document-level Neural Machine Translation (NMT) models has attracted increasing attention in recent years.
Asynchronous "Events" are Better For Motion Estimation
As an instance of event-based camera, Dynamic and Active-pixel Vision Sensor (DAVIS) combines a standard camera and an event-based camera.
Hungarian Layer: Logics Empowered Neural Architecture
However, lacking of logic flow (e. g. \textit{if, for, while}), traditional algorithms (e. g. \textit{Hungarian algorithm, A$^*$ searching, decision tress algorithm}) could not be embedded into this paradigm, which limits the theories and applications.
Deep Semantic Role Labeling with Self-Attention
Semantic Role Labeling (SRL) is believed to be a crucial step towards natural language understanding and has been widely studied.
Ranked #14 on
Semantic Role Labeling
on OntoNotes
XMU Neural Machine Translation Online Service
We demonstrate a neural machine translation web service.
XMU Neural Machine Translation Systems for WAT 2017
This paper describes the Neural Machine Translation systems of Xiamen University for the shared translation tasks of WAT 2017.
Improving Implicit Discourse Relation Recognition with Discourse-specific Word Embeddings
We introduce a simple and effective method to learn discourse-specific word embeddings (DSWE) for implicit discourse relation recognition.
Lattice-Based Recurrent Neural Network Encoders for Neural Machine Translation
Neural machine translation (NMT) heavily relies on word-level modelling to learn semantic representations of input sentences.
