Search Results for author:
Found 27 papers, 9 papers with code
A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding
In our framework, we adopt a joint model with Stack-Propagation which can directly use the intent information as input for slot filling, thus to capture the intent semantic knowledge.
Ranked #2 on
Intent Detection
on SNIPS
Empirical Analysis of Unlabeled Entity Problem in Named Entity Recognition
Experiments on synthetic datasets and real-world datasets show that our model is robust to unlabeled entity problem and surpasses prior baselines.
Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever
Querying the knowledge base (KB) has long been a challenge in the end-to-end task-oriented dialogue system.
Ranked #6 on
Task-Oriented Dialogue Systems
on KVRET
Towards Better Surgical Instrument Segmentation in Endoscopic Vision: Multi-Angle Feature Aggregation and Contour Supervision
Accurate and real-time surgical instrument segmentation is important in the endoscopic vision of robot-assisted surgery, and significant challenges are posed by frequent instrument-tissue contacts and continuous change of observation perspective.
LC-GAN: Image-to-image Translation Based on Generative Adversarial Network for Endoscopic Images
For live image segmentation, we first translate the live images to fake-cadaveric images with LC-GAN and then perform segmentation on the fake-cadaveric images with models trained on the real cadaveric dataset.
Neural Sequence Segmentation as Determining the Leftmost Segments
Prior methods to text segmentation are mostly at token level.
Fine-grained Entity Typing without Knowledge Base
Existing work on Fine-grained Entity Typing (FET) typically trains automatic models on the datasets obtained by using Knowledge Bases (KB) as distant supervision.
Simulation Results on Selector Adaptation in Behavior Trees
The "Selector" node of a BT tries alternative strategies (its children) and returns success only if all of its children return failure.
Robotics
Efficient Sub-structured Knowledge Distillation
Specifically, we transfer the knowledge from a teacher model to its student model by locally matching their predictions on all sub-structures, instead of the whole output space.
Multi-Domain Spoken Language Understanding Using Domain- and Task-Aware Parameterization
Spoken language understanding has been addressed as a supervised learning problem, where a set of training data is available for each domain.
Slot-consistent NLG for Task-oriented Dialogue Systems with Iterative Rectification Network
Data-driven approaches using neural networks have achieved promising performances in natural language generation (NLG).
Handling Rare Entities for Neural Sequence Labeling
One great challenge in neural sequence labeling is the data sparsity problem for rare entity words and phrases.
DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification
In dialog system, dialog act recognition and sentiment classification are two correlative tasks to capture speakers intentions, where dialog act and sentiment can indicate the explicit and the implicit intentions separately.
Rewriter-Evaluator Architecture for Neural Machine Translation
We apply the proposed architecture to improve the general NMT models (e. g., Transformer).
Segmenting Natural Language Sentences via Lexical Unit Analysis
In this work, we present Lexical Unit Analysis (LUA), a framework for general sequence segmentation tasks.
Interpretable NLG for Task-oriented Dialogue Systems with Heterogeneous Rendering Machines
To address this problem, we propose a novel framework, heterogeneous rendering machines (HRM), that interprets how neural generators render an input dialogue act (DA) into an utterance.
TexSmart: A Text Understanding System for Fine-Grained NER and Enhanced Semantic Analysis
This technique report introduces TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities.
Rethinking Negative Sampling for Handling Missing Entity Annotations
Negative sampling is highly effective in handling missing annotations for named entity recognition (NER).
TexSmart: A System for Enhanced Natural Language Understanding
This paper introduces TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities.
Rewriter-Evaluator Framework for Neural Machine Translation
We apply the proposed framework to improve the general NMT models (e. g., Transformer).
Real-time Virtual Intraoperative CT for Image Guided Surgery
Purpose: This paper presents a scheme for generating virtual intraoperative CT scans in order to improve surgical completeness in Endoscopic Sinus Surgeries (ESS).
Real-time Informative Surgical Skill Assessment with Gaussian Process Learning
Different with classical surgical skill assessment algorithms, the proposed method 1) utilizes the kinematic features in surgical instrument relative movements, instead of using specific surgical tasks or the statistics to assess skills in real-time; 2) provide informative feedback, instead of a summative scores; 3) has the ability to incrementally learn from new data, instead of depending on a fixed dataset.
3 Dimensional Dense Reconstruction: A Review of Algorithms and Dataset
3D dense reconstruction refers to the process of obtaining the complete shape and texture features of 3D objects from 2D planar images.
On Error Propagation of Diffusion Models
Our theoretical study also suggests that the cumulative error is closely related to the generation quality of DMs.
Soft Mixture Denoising: Beyond the Expressive Bottleneck of Diffusion Models
In light of our theoretical studies, we introduce soft mixture denoising (SMD), an expressive and efficient model for backward denoising.
TS-Diffusion: Generating Highly Complex Time Series with Diffusion Models
While current generative models have achieved promising performances in time-series synthesis, they either make strong assumptions on the data format (e. g., regularities) or rely on pre-processing approaches (e. g., interpolations) to simplify the raw data.
Risk-Sensitive Diffusion for Perturbation-Robust Optimization
The essence of score-based generative models (SGM) is to optimize a score-based model towards the score function.
