Search Results for author:
Found 41 papers, 17 papers with code
Personal Information Leakage Detection in Conversations
In this work, we propose to protect personal information by warning users of detected suspicious sentences generated by conversational assistants.
FACTUAL: A Benchmark for Faithful and Consistent Textual Scene Graph Parsing
Textual scene graph parsing has become increasingly important in various vision-language applications, including image caption evaluation and image retrieval.
The Best of Both Worlds: Combining Human and Machine Translations for Multilingual Semantic Parsing with Active Learning
Multilingual semantic parsing aims to leverage the knowledge from the high-resource languages to improve low-resource semantic parsing, yet commonly suffers from the data imbalance problem.
Language Independent Neuro-Symbolic Semantic Parsing for Form Understanding
Our model parses a form into a word-relation graph in order to identify entities and relations jointly and reduce the time complexity of inference.
Turning Flowchart into Dialog: Plan-based Data Augmentation for Low-Resource Flowchart-grounded Troubleshooting Dialogs
Flowchart-grounded troubleshooting dialogue (FTD) systems, which follow the instructions of a flowchart to diagnose users' problems in specific domains (eg., vehicle, laptop), have been gaining research interest in recent years.
SocialDial: A Benchmark for Socially-Aware Dialogue Systems
To the best of our knowledge, SocialDial is the first socially-aware dialogue dataset that covers multiple social factors and has fine-grained labels.
Cultural Vocal Bursts Intensity Prediction
Synthetic Data Generation
Less is More: Mitigate Spurious Correlations for Open-Domain Dialogue Response Generation Models by Causal Discovery
In this paper, we conduct the first study on spurious correlations for open-domain response generation models based on a corpus CGDIALOG curated in our work.
Document Flattening: Beyond Concatenating Context for Document-Level Neural Machine Translation
Existing work in document-level neural machine translation commonly concatenates several consecutive sentences as a pseudo-document, and then learns inter-sentential dependencies.
When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning Methods
To facilitate the research of PETuning in FL, we also develop a federated tuning framework FedPETuning, which allows practitioners to exploit different PETuning methods under the FL training paradigm conveniently.
Let's Negotiate! A Survey of Negotiation Dialogue Systems
Negotiation is one of the crucial abilities in human communication, and there has been a resurgent research interest in negotiation dialogue systems recently, which goal is to empower intelligent agents with such ability that can efficiently help humans resolve conflicts or reach beneficial agreements.
Learning Object-Language Alignments for Open-Vocabulary Object Detection
In this paper, we propose a novel open-vocabulary object detection framework directly learning from image-text pair data.
ViLPAct: A Benchmark for Compositional Generalization on Multimodal Human Activities
We introduce ViLPAct, a novel vision-language benchmark for human activity planning.
Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generation
In this paper, we propose a variational autoencoder with disentanglement priors, VAE-DPRIOR, for task-specific natural language generation with none or a handful of task-specific labeled examples.
Multimodal Transformer with Variable-length Memory for Vision-and-Language Navigation
Vision-and-Language Navigation (VLN) is a task that an agent is required to follow a language instruction to navigate to the goal position, which relies on the ongoing interactions with the environment during moving.
Simple or Complex? Complexity-Controllable Question Generation with Soft Templates and Deep Mixture of Experts Model
The ability to generate natural-language questions with controlled complexity levels is highly desirable as it further expands the applicability of question generation.
Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers
We conduct extensive experiments to study the research problems involved in continual semantic parsing and demonstrate that a neural semantic parser trained with TotalRecall achieves superior performance than the one trained directly with the SOTA continual learning algorithms and achieve a 3-6 times speedup compared to re-training from scratch.
Student Surpasses Teacher: Imitation Attack for Black-Box NLP APIs
Machine-learning-as-a-service (MLaaS) has attracted millions of users to their splendid large-scale models.
Neural-Symbolic Commonsense Reasoner with Relation Predictors
The dynamic nature of commonsense knowledge postulates models capable of performing multi-hop reasoning over new situations.
On Robustness of Neural Semantic Parsers
In this paper, we provide the empirical study on the robustness of semantic parsers in the presence of adversarial attacks.
Few-Shot Semantic Parsing for New Predicates
In this work, we investigate the problems of semantic parsing in a few-shot learning setting.
Context Dependent Semantic Parsing: A Survey
Semantic parsing is the task of translating natural language utterances into machine-readable meaning representations.
COSMO: Conditional SEQ2SEQ-based Mixture Model for Zero-Shot Commonsense Question Answering
However, current approaches in this realm lack the ability to perform commonsense reasoning upon facing an unseen situation, mostly due to incapability of identifying a diverse range of implicit social relations.
Privacy-Aware Text Rewriting
Biased decisions made by automatic systems have led to growing concerns in research communities.
ALTER: Auxiliary Text Rewriting Tool for Natural Language Generation
In this paper, we describe ALTER, an auxiliary text rewriting tool that facilitates the rewriting process for natural language generation tasks, such as paraphrasing, text simplification, fairness-aware text rewriting, and text style transfer.
Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection
Due to the sharp increase in the severity of the threat imposed by software vulnerabilities, the detection of vulnerabilities in binary code has become an important concern in the software industry, such as the embedded systems industry, and in the field of computer security.
D-PAGE: Diverse Paraphrase Generation
In this paper, we investigate the diversity aspect of paraphrase generation.
f-GANs in an Information Geometric Nutshell
In this paper, we unveil a broad class of distributions for which such convergence happens --- namely, deformed exponential families, a wide superset of exponential families --- and show tight connections with the three other key GAN parameters: loss, game and architecture.
Demographic Inference on Twitter using Recursive Neural Networks
In social media, demographic inference is a critical task in order to gain a better understanding of a cohort and to facilitate interacting with one{'}s audience.
Collective Vertex Classification Using Recursive Neural Network
In this paper, we propose a graph-based recursive neural network framework for collective vertex classification.
Automatic Generation of Grounded Visual Questions
In this paper, we propose the first model to be able to generate visually grounded questions with diverse types for a single image.
Named Entity Recognition for Novel Types by Transfer Learning
In named entity recognition, we often don't have a large in-domain training corpus or a knowledge base with adequate coverage to train a model directly.
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
We present a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class-dependent label noise.
Ranked #2 on
Image Classification
on Clothing1M (using clean data)
(using extra training data)
STransE: a novel embedding model of entities and relationships in knowledge bases
Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks.
Neighborhood Mixture Model for Knowledge Base Completion
Knowledge bases are useful resources for many natural language processing tasks, however, they are far from complete.
Big Data Small Data, In Domain Out-of Domain, Known Word Unknown Word: The Impact of Word Representation on Sequence Labelling Tasks
Word embeddings -- distributed word representations that can be learned from unlabelled data -- have been shown to have high utility in many natural language processing applications.
Estimating Maximally Probable Constrained Relations by Mathematical Programming
Estimating (learning) a maximally probable measure, given (a training set of) related and unrelated pairs, is a convex optimization problem.
Senti-LSSVM: Sentiment-Oriented Multi-Relation Extraction with Latent Structural SVM
Extracting instances of sentiment-oriented relations from user-generated web documents is important for online marketing analysis.
