Search Results for author:
Found 77 papers, 27 papers with code
Data-driven, PCFG-based and Pseudo-PCFG-based Models for Chinese Dependency Parsing
We present a comparative study of transition-, graph- and PCFG-based models aimed at illuminating more precisely the likely contribution of CFGs in improving Chinese dependency parsing accuracy, especially by combining heterogeneous models.
Parsing to 1-Endpoint-Crossing, Pagenumber-2 Graphs
We study the Maximum Subgraph problem in deep dependency parsing.
Semantic Dependency Parsing via Book Embedding
We model a dependency graph as a book, a particular kind of topological space, for semantic dependency parsing.
The Covert Helps Parse the Overt
This paper is concerned with whether deep syntactic information can help surface parsing, with a particular focus on empty categories.
Parsing for Grammatical Relations via Graph Merging
This paper is concerned with building deep grammatical relation (GR) analysis using data-driven approach.
Quasi-Second-Order Parsing for 1-Endpoint-Crossing, Pagenumber-2 Graphs
We propose a new Maximum Subgraph algorithm for first-order parsing to 1-endpoint-crossing, pagenumber-2 graphs.
Complex Structure Leads to Overfitting: A Structure Regularization Decoding Method for Natural Language Processing
The decoding of the complex structure model is regularized by the additionally trained simple structure model.
DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision
Estimating the travel time of a path is of great importance to smart urban mobility.
Language Generation via DAG Transduction
This remarkable result demonstrates the feasibility of applying a DAG transducer to resolve NLG, as well as the effectiveness of our design.
Pre- and In-Parsing Models for Neural Empty Category Detection
Motivated by the positive impact of empty category on syntactic parsing, we study neural models for pre- and in-parsing detection of empty category, which has not previously been investigated.
Accurate SHRG-Based Semantic Parsing
We demonstrate that an SHRG-based parser can produce semantic graphs much more accurately than previously shown, by relating synchronous production rules to the syntacto-semantic composition process.
Semantic Role Labeling for Learner Chinese: the Importance of Syntactic Parsing and L2-L1 Parallel Data
This paper studies semantic parsing for interlanguage (L2), taking semantic role labeling (SRL) as a case task and learner Chinese as a case language.
Neural Maximum Subgraph Parsing for Cross-Domain Semantic Dependency Analysis
We present experiments for cross-domain semantic dependency analysis with a neural Maximum Subgraph parser.
Ineffectiveness of Dictionary Coding to Infer Predictability Limits of Human Mobility
Consequently, it is ineffective to infer predictability through dictionary coding algorithms.
Linearized Multi-Sampling for Differentiable Image Transformation
We propose a novel image sampling method for differentiable image transformation in deep neural networks.
Parsing Chinese Sentences with Grammatical Relations
In this work, we propose to represent grammatical information using general directed dependency graphs.
UniVSE: Robust Visual Semantic Embeddings via Structured Semantic Representations
We propose Unified Visual-Semantic Embeddings (UniVSE) for learning a joint space of visual and textual concepts.
CUNY-PKU Parser at SemEval-2019 Task 1: Cross-Lingual Semantic Parsing with UCCA
This paper describes the systems of the CUNY-PKU team in SemEval 2019 Task 1: Cross-lingual Semantic Parsing with UCCA.
Graph-Based Meaning Representations: Design and Processing
This tutorial is on representing and processing sentence meaning in the form of labeled directed graphs.
A Comparative Analysis of Knowledge-Intensive and Data-Intensive Semantic Parsers
We present a phenomenon-oriented comparative analysis of the two dominant approaches in task-independent semantic parsing: classic, knowledge-intensive and neural, data-intensive models.
ACNe: Attentive Context Normalization for Robust Permutation-Equivariant Learning
Many problems in computer vision require dealing with sparse, unordered data in the form of point clouds.
Optimizing Through Learned Errors for Accurate Sports Field Registration
We propose an optimization-based framework to register sports field templates onto broadcast videos.
Large-scale Gastric Cancer Screening and Localization Using Multi-task Deep Neural Network
Gastric cancer is one of the most common cancers, which ranks third among the leading causes of cancer death.
Peking at MRP 2019: Factorization- and Composition-Based Parsing for Elementary Dependency Structures
We design, implement and evaluate two semantic parsers, which represent factorization- and composition-based approaches respectively, for Elementary Dependency Structures (EDS) at the CoNLL 2019 Shared Task on Cross-Framework Meaning Representation Parsing.
DeepDualMapper: A Gated Fusion Network for Automatic Map Extraction using Aerial Images and Trajectories
We propose a deep convolutional neural network called DeepDualMapper which fuses the aerial image and trajectory data in a more seamless manner to extract the digital map.
PSDet: Efficient and Universal Parking Slot Detection
\romannumeral2, Expert knowledge for parking slot detection is under-estimated.
Vehicle Re-ID for Surround-view Camera System
The vehicle re-identification (ReID) plays a critical role in the perception system of autonomous driving, which attracts more and more attention in recent years.
Parsing into Variable-in-situ Logico-Semantic Graphs
We propose variable-in-situ logico-semantic graphs to bridge the gap between semantic graph and logical form parsing.
Semantic Parsing for English as a Second Language
This paper is concerned with semantic parsing for English as a second language (ESL).
Exact yet Efficient Graph Parsing, Bi-directional Locality and the Constructivist Hypothesis
A key problem in processing graph-based meaning representations is graph parsing, i. e. computing all possible derivations of a given graph according to a (competence) grammar.
Constructing a Family Tree of Ten Indo-European Languages with Delexicalized Cross-linguistic Transfer Patterns
It is reasonable to hypothesize that the divergence patterns formulated by historical linguists and typologists reflect constraints on human languages, and are thus consistent with Second Language Acquisition (SLA) in a certain way.
Multiscale Context-Aware Ensemble Deep KELM for Efficient Hyperspectral Image Classification
Recently, multiscale spatial features have been widely utilized to improve the hyperspectral image (HSI) classification performance.
LiteDepthwiseNet: An Extreme Lightweight Network for Hyperspectral Image Classification
Based on 3D depthwise convolution, LiteDepthwiseNet can decompose standard convolution into depthwise convolution and pointwise convolution, which can achieve high classification performance with minimal parameters.
Canonical Capsules: Self-Supervised Capsules in Canonical Pose
We propose a self-supervised capsule architecture for 3D point clouds.
Simulating User Satisfaction for the Evaluation of Task-oriented Dialogue Systems
The purpose of the task is to increase the evaluation power of user simulations and to make the simulation more human-like.
Universal Semantic Tagging for English and Mandarin Chinese
By means of the new annotations, we also evaluate a series of neural tagging models to gauge how successful semantic tagging can be: accuracies of 92. 7{\%} and 94. 6{\%} are obtained for Chinese and English respectively.
Metaphorical User Simulators for Evaluating Task-oriented Dialogue Systems
Employing existing user simulators to evaluate TDSs is challenging as user simulators are primarily designed to optimize dialogue policies for TDSs and have limited evaluation capabilities.
Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method
How to coordinate the communication among intersections effectively in real complex traffic scenarios with multi-intersection is challenging.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
TUSK: Task-Agnostic Unsupervised Keypoints
Existing unsupervised methods for keypoint learning rely heavily on the assumption that a specific keypoint type (e. g. elbow, digit, abstract geometric shape) appears only once in an image.
Adaptive Structural Similarity Preserving for Unsupervised Cross Modal Hashing
We introduce structural semantic metrics based on graph adjacency relations during the semantic reconstruction and correlation mining stage and meanwhile align the structure semantics in the hash space with an asymmetric binary optimization process.
NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds
We introduce a method for instance proposal generation for 3D point clouds.
Towards General-Purpose Representation Learning of Polygonal Geometries
For the spatial domain approach, we propose ResNet1D, a 1D CNN-based polygon encoder, which uses circular padding to achieve loop origin invariance on simple polygons.
RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer
However, many real-life trajectories are collected with low sample rate due to energy concern or other constraints. We study the task of trajectory recovery in this paper as a means for increasing the sample rate of low sample trajectories.
CAT: Learning to Collaborate Channel and Spatial Attention from Multi-Information Fusion
Channel and spatial attention mechanism has proven to provide an evident performance boost of deep convolution neural networks (CNNs).
Contrastive Learning Reduces Hallucination in Conversations
MixCL effectively reduces the hallucination of LMs in conversations and achieves the highest performance among LM-based dialogue agents in terms of relevancy and factuality.
Generative Knowledge Selection for Knowledge-Grounded Dialogues
However, such approaches neglect the interactions between snippets, leading to difficulties in inferring the meaning of snippets.
Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents
In this paper, we first investigate generative LLMs such as ChatGPT and GPT-4 for relevance ranking in IR.
Towards Explainable Conversational Recommender Systems
To achieve this, we conduct manual and automatic approaches to extend these dialogues and construct a new CRS dataset, namely Explainable Recommendation Dialogues (E-ReDial).
Answering Ambiguous Questions via Iterative Prompting
To provide feasible answers to an ambiguous question, one approach is to directly predict all valid answers, but this can struggle with balancing relevance and diversity.
Is Argument Structure of Learner Chinese Understandable: A Corpus-Based Analysis
The annotation procedure is guided by the Chinese PropBank specification, which is originally developed to cover first language phenomena.
RADE: Reference-Assisted Dialogue Evaluation for Open-Domain Dialogue
To this end, we propose the Reference-Assisted Dialogue Evaluation (RADE) approach under the multi-task learning framework, which leverages the pre-created utterance as reference other than the gold response to relief the one-to-many problem.
SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution
Existing digital sensors capture images at fixed spatial and spectral resolutions (e. g., RGB, multispectral, and hyperspectral images), and each combination requires bespoke machine learning models.
Recoverable Privacy-Preserving Image Classification through Noise-like Adversarial Examples
Extensive experiments demonstrate that 1) the classification accuracy of the classifier trained in the plaintext domain remains the same in both the ciphertext and plaintext domains; 2) the encrypted images can be recovered into their original form with an average PSNR of up to 51+ dB for the SVHN dataset and 48+ dB for the VGGFace2 dataset; 3) our system exhibits satisfactory generalization capability on the encryption, decryption and classification tasks across datasets that are different from the training one; and 4) a high-level of security is achieved against three potential threat models.
Generating Robust Adversarial Examples against Online Social Networks (OSNs)
In this work, we aim to design a new framework for generating robust AEs that can survive the OSN transmission; namely, the AEs before and after the OSN transmission both possess strong attack capabilities.
DiQAD: A Benchmark Dataset for End-to-End Open-domain Dialogue Assessment
Dialogue assessment plays a critical role in the development of open-domain dialogue systems.
Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method
In this paper, we propose a novel self-detection method to detect which questions that a LLM does not know that are prone to generate nonfactual results.
Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers
Furthermore, our approach surpasses the performance of existing supervised methods like monoT5 and is on par with the state-of-the-art zero-shot methods.
PointNeRF++: A multi-scale, point-based Neural Radiance Field
We overcome these problems with a simple representation that aggregates point clouds at multiple scale levels with sparse voxel grids at different resolutions.
Densify Your Labels: Unsupervised Clustering with Bipartite Matching for Weakly Supervised Point Cloud Segmentation
We propose a weakly supervised semantic segmentation method for point clouds that predicts "per-point" labels from just "whole-scene" annotations while achieving the performance of recent fully supervised approaches.
ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling
A wide range of experiments on both synthetic and real-world datasets have illustrated the superior modeling capacities and prediction performance of ContiFormer on irregular time series data.
How Large Language Models Encode Context Knowledge? A Layer-Wise Probing Study
Previous work has showcased the intriguing capability of large language models (LLMs) in retrieving facts and processing context knowledge.
Improving the Robustness of Large Language Models via Consistency Alignment
The training process is accomplished by self-rewards inferred from the trained model at the first stage without referring to external human preference resources.
Enhanced Generative Recommendation via Content and Collaboration Integration
However, existing generative recommendation approaches still encounter challenges in (i) effectively integrating user-item collaborative signals and item content information within a unified generative framework, and (ii) executing an efficient alignment between content information and collaborative signals.
3D Gaussian Splatting as Markov Chain Monte Carlo
While 3D Gaussian Splatting has recently become popular for neural rendering, current methods rely on carefully engineered cloning and splitting strategies for placing Gaussians, which does not always generalize and may lead to poor-quality renderings.
Comparing Knowledge-Intensive and Data-Intensive Models for English Resource Semantic Parsing
Abstract In this work, we present a phenomenon-oriented comparative analysis of the two dominant approaches in English Resource Semantic (ERS) parsing: classic, knowledge-intensive and neural, data-intensive models.
Negation Scope Resolution for Chinese as a Second Language
We build a neural NSR system, which achieves a new state-of-the-art accuracy on English benchmark data.
Coding Textual Inputs Boosts the Accuracy of Neural Networks
Natural Language Processing (NLP) tasks are usually performed word by word on textual inputs.
