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Found 47 papers, 22 papers with code
Improving Grammatical Error Correction for Multiword Expressions
Grammatical error correction (GEC) is the task of automatically correcting errors in text.
Document-level grammatical error correction
Document-level context can provide valuable information in grammatical error correction (GEC), which is crucial for correcting certain errors and resolving inconsistencies.
Multi-Class Grammatical Error Detection for Correction: A Tale of Two Systems
In this paper, we show how a multi-class grammatical error detection (GED) system can be used to improve grammatical error correction (GEC) for English.
Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights
If yes, we benchmark these existing adapters, which have been shown to be effective in NLP and CV tasks, in the item recommendation settings.
Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights
Furthermore, we compare the performance of the TCF paradigm utilizing the most powerful LMs to the currently dominant ID embedding-based paradigm and investigate the transferability of this TCF paradigm.
Multi-crop Contrastive Learning for Unsupervised Image-to-Image Translation
Recently, image-to-image translation methods based on contrastive learning achieved state-of-the-art results in many tasks.
Spectral Normalized Dual Contrastive Regularization for Image-to-Image Translation
In order to improve the global structure information of the generated images, we formulate a semantically contrastive loss to make the global semantic structure of the generated images similar to the real images from the target domain in the semantic feature space.
RRHF: Rank Responses to Align Language Models with Human Feedback without tears
Reinforcement Learning from Human Feedback (RLHF) facilitates the alignment of large language models with human preferences, significantly enhancing the quality of interactions between humans and these models.
Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited
In fact, this question was answered ten years ago when IDRec beats MoRec by a strong margin in both recommendation accuracy and efficiency.
Exploring Partial Knowledge Base Inference in Biomedical Entity Linking
Biomedical entity linking (EL) consists of named entity recognition (NER) and named entity disambiguation (NED).
How well do Large Language Models perform in Arithmetic tasks?
Large language models have emerged abilities including chain-of-thought to answer math word problems step by step.
RAMM: Retrieval-augmented Biomedical Visual Question Answering with Multi-modal Pre-training
We propose to retrieve similar image-text pairs based on ITC from pretraining datasets and introduce a novel retrieval-attention module to fuse the representation of the image and the question with the retrieved images and texts.
An Extended Sequence Tagging Vocabulary for Grammatical Error Correction
We extend a current sequence-tagging approach to Grammatical Error Correction (GEC) by introducing specialised tags for spelling correction and morphological inflection using the SymSpell and LemmInflect algorithms.
Molecular Geometry-aware Transformer for accurate 3D Atomic System modeling
To alleviate this limitation, we propose Moleformer, a novel Transformer architecture that takes nodes (atoms) and edges (bonds and nonbonding atom pairs) as inputs and models the interactions among them using rotational and translational invariant geometry-aware spatial encoding.
SeqDiffuSeq: Text Diffusion with Encoder-Decoder Transformers
We propose SeqDiffuSeq, a text diffusion model for sequence-to-sequence generation.
HyPe: Better Pre-trained Language Model Fine-tuning with Hidden Representation Perturbation
Unlike previous works that only add noise to inputs or parameters, we argue that the hidden representations of Transformers layers convey more diverse and meaningful language information.
Grammatical Error Correction: A Survey of the State of the Art
Grammatical Error Correction (GEC) is the task of automatically detecting and correcting errors in text.
Graded-Q Reinforcement Learning with Information-Enhanced State Encoder for Hierarchical Collaborative Multi-Vehicle Pursuit
However, existing works on MVP pay little attention to the importance of information exchange and cooperation among pursuing vehicles under the complex urban traffic environment.
Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning
Entities lie in the heart of biomedical natural language understanding, and the biomedical entity linking (EL) task remains challenging due to the fine-grained and diversiform concept names.
Confidence Estimation Transformer for Long-term Renewable Energy Forecasting in Reinforcement Learning-based Power Grid Dispatching
The expansion of renewable energy could help realizing the goals of peaking carbon dioxide emissions and carbon neutralization.
BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model
Furthermore, we conduct ablation studies on the pretraining tasks for BioBART and find that sentence permutation has negative effects on downstream tasks.
Ranked #3 on
Entity Linking
on MedMentions
Automatic Biomedical Term Clustering by Learning Fine-grained Term Representations
Term clustering is important in biomedical knowledge graph construction.
BIOS: An Algorithmically Generated Biomedical Knowledge Graph
For decades, these knowledge graphs have been developed via expert curation; however, this method can no longer keep up with today's AI development, and a transition to algorithmically generated BioMedKGs is necessary.
Code Synonyms Do Matter: Multiple Synonyms Matching Network for Automatic ICD Coding
Automatic ICD coding is defined as assigning disease codes to electronic medical records (EMRs).
Ranked #5 on
Medical Code Prediction
on MIMIC-III
$ \text{T}^3 $OMVP: A Transformer-based Time and Team Reinforcement Learning Scheme for Observation-constrained Multi-Vehicle Pursuit in Urban Area
Although there are some achievements in the field of MVP in the open space environment, the urban area brings complicated road structures and restricted moving spaces as challenges to the resolution of MVP games.
Adaptive Perturbation for Adversarial Attack
Instead of using the sign function, we propose to directly utilize the exact gradient direction with a scaling factor for generating adversarial perturbations, which improves the attack success rates of adversarial examples even with fewer perturbations.
Adaptive Image Transformations for Transfer-based Adversarial Attack
Adversarial attacks provide a good way to study the robustness of deep learning models.
Fusing Heterogeneous Factors with Triaffine Mechanism for Nested Named Entity Recognition
To fuse these heterogeneous factors, we propose a novel triaffine mechanism including triaffine attention and scoring.
Ranked #1 on
Nested Named Entity Recognition
on TAC-KBP 2017
MetaHistoSeg: A Python Framework for Meta Learning in Histopathology Image Segmentation
We also curate a histopathology meta dataset - a benchmark dataset for training and validating models on out-of-distribution performance across a range of cancer types.
Meta Gradient Adversarial Attack
In recent years, research on adversarial attacks has become a hot spot.
Cambridge at SemEval-2021 Task 2: Neural WiC-Model with Data Augmentation and Exploration of Representation
This paper describes the system of the Cambridge team submitted to the SemEval-2021 shared task on Multilingual and Cross-lingual Word-in-Context Disambiguation.
Cambridge at SemEval-2021 Task 1: An Ensemble of Feature-Based and Neural Models for Lexical Complexity Prediction
This paper describes our submission to the SemEval-2021 shared task on Lexical Complexity Prediction.
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice.
Ranked #1 on
Medical Relation Extraction
on CMeIE
Improving Biomedical Pretrained Language Models with Knowledge
To this end, we propose KeBioLM, a biomedical pretrained language model that explicitly leverages knowledge from the UMLS knowledge bases.
Ranked #1 on
Named Entity Recognition (NER)
on JNLPBA
Biomedical Question Answering: A Survey of Approaches and Challenges
Automatic Question Answering (QA) has been successfully applied in various domains such as search engines and chatbots.
Attributes Aware Face Generation with Generative Adversarial Networks
Recent studies have shown remarkable success in face image generations.
CODER: Knowledge infused cross-lingual medical term embedding for term normalization
This paper proposes CODER: contrastive learning on knowledge graphs for cross-lingual medical term representation.
Neural and FST-based approaches to grammatical error correction
In this paper, we describe our submission to the BEA 2019 shared task on grammatical error correction.
Neural sequence modelling for learner error prediction
This paper describes our use of two recurrent neural network sequence models: sequence labelling and sequence-to-sequence models, for the prediction of future learner errors in our submission to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM).
Construction of the Literature Graph in Semantic Scholar
We describe a deployed scalable system for organizing published scientific literature into a heterogeneous graph to facilitate algorithmic manipulation and discovery.
Neural Sequence-Labelling Models for Grammatical Error Correction
We propose an approach to N-best list reranking using neural sequence-labelling models.
Artificial Error Generation with Machine Translation and Syntactic Patterns
Shortage of available training data is holding back progress in the area of automated error detection.
Ranked #3 on
Grammatical Error Detection
on FCE
