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Found 20 papers, 5 papers with code
Are Pre-trained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection
Pre-trained Transformer-based models were reported to be robust in intent classification.
Uncertainty Quantification on Graph Learning: A Survey
Graphical models, including Graph Neural Networks (GNNs) and Probabilistic Graphical Models (PGMs), have demonstrated their exceptional capabilities across numerous fields.
Beyond the Known: Novel Class Discovery for Open-world Graph Learning
Inter-class correlations are subsequently eliminated by the prototypical attention network, leading to distinctive representations for different classes.
Motif-aware Riemannian Graph Neural Network with Generative-Contrastive Learning
In light of the issues above, we propose the problem of \emph{Motif-aware Riemannian Graph Representation Learning}, seeking a numerically stable encoder to capture motif regularity in a diverse-curvature manifold without labels.
A Counterfactual Fair Model for Longitudinal Electronic Health Records via Deconfounder
The fairness issue of clinical data modeling, especially on Electronic Health Records (EHRs), is of utmost importance due to EHR's complex latent structure and potential selection bias.
Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders
Considering the influence of historical purchases on users' future interests, the user and item representations can be viewed as unobserved confounders in the causal diagram.
Continuous Prompt Tuning Based Textual Entailment Model for E-commerce Entity Typing
In order to handle new entities in product titles and address the special language styles problem of product titles in e-commerce domain, we propose our textual entailment model with continuous prompt tuning based hypotheses and fusion embeddings for e-commerce entity typing.
Mitigating Health Disparities in EHR via Deconfounder
First, these methods usually mean a trade-off between the model's performance and fairness.
Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning
In this work, we focus on a more challenging few-shot intent detection scenario where many intents are fine-grained and semantically similar.
Pseudo Siamese Network for Few-shot Intent Generation
PSN consists of two identical subnetworks with the same structure but different weights: an action network and an object network.
Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System
Two major challenges exist in this new task: (i) For the learning process, the system should incrementally learn new classes round by round without re-training on the examples of preceding classes; (ii) For the performance, the system should perform well on new classes without much loss on preceding classes.
Enriching Non-Autoregressive Transformer with Syntactic and Semantic Structures for Neural Machine Translation
In this paper, we claim that the syntactic and semantic structures among natural language are critical for non-autoregressive machine translation and can further improve the performance.
Improving Medical NLI Using Context-Aware Domain Knowledge
Domain knowledge is important to understand both the lexical and relational associations of words in natural language text, especially for domain-specific tasks like Natural Language Inference (NLI) in the medical domain, where due to the lack of a large annotated dataset such knowledge cannot be implicitly learned during training.
Composed Variational Natural Language Generation for Few-shot Intents
In this paper, we focus on generating training examples for few-shot intents in the realistic imbalanced scenario.
Multi-label Zero-shot Classification by Learning to Transfer from External Knowledge
On the other hand, there is a large semantic gap between seen and unseen classes in the existing multi-label classification datasets.
CG-BERT: Conditional Text Generation with BERT for Generalized Few-shot Intent Detection
In this paper, we formulate a more realistic and difficult problem setup for the intent detection task in natural language understanding, namely Generalized Few-Shot Intent Detection (GFSID).
MZET: Memory Augmented Zero-Shot Fine-grained Named Entity Typing
Named entity typing (NET) is a classification task of assigning an entity mention in the context with given semantic types.
Learn to Forget: Machine Unlearning via Neuron Masking
To this end, machine unlearning becomes a popular research topic, which allows users to eliminate memorization of their private data from a trained machine learning model. In this paper, we propose the first uniform metric called for-getting rate to measure the effectiveness of a machine unlearning method.
Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT
There is an increasing amount of literature that claims the brittleness of deep neural networks in dealing with adversarial examples that are created maliciously.
Multi-Grained Named Entity Recognition
This paper presents a novel framework, MGNER, for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested.
Ranked #5 on
Nested Mention Recognition
on ACE 2005
Multi-Grained Named Entity Recognition
named-entity-recognition
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