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Found 18 papers, 8 papers with code
xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein
We propose a unified protein language model, xTrimoPGLM, to address these two types of tasks simultaneously through an innovative pre-training framework.
Linker-Tuning: Optimizing Continuous Prompts for Heterodimeric Protein Prediction
Predicting the structure of interacting chains is crucial for understanding biological systems and developing new drugs.
xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data
Advances in high-throughput sequencing technology have led to significant progress in measuring gene expressions at the single-cell level.
Won't Get Fooled Again: Answering Questions with False Premises
In this paper, we find that the PLMs already possess the knowledge required to rebut such questions, and the key is how to activate the knowledge.
Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations
Then we introduce BOSS, a Benchmark suite for Out-of-distribution robustneSS evaluation covering 5 tasks and 20 datasets.
K-AID: Enhancing Pre-trained Language Models with Domain Knowledge for Question Answering
Knowledge enhanced pre-trained language models (K-PLMs) are shown to be effective for many public tasks in the literature but few of them have been successfully applied in practice.
Evaluating Modules in Graph Contrastive Learning
The recent emergence of contrastive learning approaches facilitates the application on graph representation learning (GRL), introducing graph contrastive learning (GCL) into the literature.
Dual-View Distilled BERT for Sentence Embedding
In this paper, we propose a Dual-view distilled BERT~(DvBERT) for sentence matching with sentence embeddings.
Towards Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning
The ambiguous annotation criteria lead to divergence of Chinese Word Segmentation (CWS) datasets in various granularities.
Question Directed Graph Attention Network for Numerical Reasoning over Text
Numerical reasoning over texts, such as addition, subtraction, sorting and counting, is a challenging machine reading comprehension task, since it requires both natural language understanding and arithmetic computation.
Ranked #1 on
Question Answering
on DROP Test
A Workflow for Exploring Ligand Dissociation from a Macromolecule: Efficient Random Acceleration Molecular Dynamics Simulation and Interaction Fingerprints Analysis of Ligand Trajectories
The combined implementation of RAMD and MD-IFP provides a computationally efficient and freely available workflow that can be applied to hundreds of compounds in a reasonable computational time and will facilitate the use of tauRAMD in drug design.
SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check
This paper proposes to incorporate phonological and visual similarity knowledge into language models for CSC via a specialized graph convolutional network (SpellGCN).
Symmetric Regularization based BERT for Pair-wise Semantic Reasoning
To remedy this, we propose to augment the NSP task to a 3-class categorization task, which includes a category for previous sentence prediction (PSP).
BERT-Based Multi-Head Selection for Joint Entity-Relation Extraction
Combining these three contributions, we enhance the information extracting ability of the multi-head selection model and achieve F1-score 0. 876 on testset-1 with a single model.
An end-to-end Neural Network Framework for Text Clustering
Conventional \mbox{methods} generally treat this task using separated steps, including text representation learning and clustering the representations.
Toward Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning
The ambiguous annotation criteria lead to divergence of Chinese Word Segmentation (CWS) datasets in various granularities.
Variational Semi-supervised Aspect-term Sentiment Analysis via Transformer
By disentangling the latent representation into the aspect-specific sentiment and the lexical context, our method induces the underlying sentiment prediction for the unlabeled data, which then benefits the ATSA classifier.
Aspect-Based Sentiment Analysis (ABSA)
Natural Language Understanding
+1
DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting
Several pioneering approaches have been proposed based on traffic observations of the target location as well as its adjacent regions, but they obtain somewhat limited accuracy due to a lack of mining road topology.
