Search Results for author:
Found 42 papers, 12 papers with code
Eraser: Jailbreaking Defense in Large Language Models via Unlearning Harmful Knowledge
Jailbreaking attacks can enable Large Language Models (LLMs) to bypass the safeguard and generate harmful content.
Weakly Supervised Reasoning by Neuro-Symbolic Approaches
Deep learning has largely improved the performance of various natural language processing (NLP) tasks.
Self-Balanced Dropout
Dropout is known as an effective way to reduce overfitting via preventing co-adaptations of units.
A Prism Module for Semantic Disentanglement in Name Entity Recognition
To solve this problem, we propose a prism module to disentangle the semantic aspects of words and reduce noise at the input layer of a model.
Ranked #52 on
Named Entity Recognition (NER)
on CoNLL 2003 (English)
Neural Entity Reasoner for Global Consistency in NER
Given an input sentence, the NE-Reasoner layer can infer over multiple entities to increase the global consistency of output labels, which then be transfered into entities for the input of next layer.
Generalize Symbolic Knowledge With Neural Rule Engine
As neural networks have dominated the state-of-the-art results in a wide range of NLP tasks, it attracts considerable attention to improve the performance of neural models by integrating symbolic knowledge.
JUMPER: Learning When to Make Classification Decisions in Reading
Both the classification result and when to make the classification are part of the decision process, which is controlled by a policy network and trained with reinforcement learning.
Event Identification as a Decision Process with Non-linear Representation of Text
We propose scale-free Identifier Network(sfIN), a novel model for event identification in documents.
Object-oriented Neural Programming (OONP) for Document Understanding
We propose Object-oriented Neural Programming (OONP), a framework for semantically parsing documents in specific domains.
Deep Neural Machine Translation with Linear Associative Unit
Deep Neural Networks (DNNs) have provably enhanced the state-of-the-art Neural Machine Translation (NMT) with their capability in modeling complex functions and capturing complex linguistic structures.
Coupling Distributed and Symbolic Execution for Natural Language Queries
Building neural networks to query a knowledge base (a table) with natural language is an emerging research topic in deep learning.
Neural Machine Translation Advised by Statistical Machine Translation
Neural Machine Translation (NMT) is a new approach to machine translation that has made great progress in recent years.
Interactive Attention for Neural Machine Translation
Conventional attention-based Neural Machine Translation (NMT) conducts dynamic alignment in generating the target sentence.
Context Gates for Neural Machine Translation
In neural machine translation (NMT), generation of a target word depends on both source and target contexts.
Memory-enhanced Decoder for Neural Machine Translation
We propose to enhance the RNN decoder in a neural machine translator (NMT) with external memory, as a natural but powerful extension to the state in the decoding RNN.
Neural Machine Translation with External Phrase Memory
In this paper, we propose phraseNet, a neural machine translator with a phrase memory which stores phrase pairs in symbolic form, mined from corpus or specified by human experts.
Incorporating Copying Mechanism in Sequence-to-Sequence Learning
CopyNet can nicely integrate the regular way of word generation in the decoder with the new copying mechanism which can choose sub-sequences in the input sequence and put them at proper places in the output sequence.
Modeling Coverage for Neural Machine Translation
Attention mechanism has enhanced state-of-the-art Neural Machine Translation (NMT) by jointly learning to align and translate.
Neural Generative Question Answering
Empirical study shows the proposed model can effectively deal with the variations of questions and answers, and generate right and natural answers by referring to the facts in the knowledge-base.
Neural Enquirer: Learning to Query Tables with Natural Language
Neural Enquirer can be trained with gradient descent, with which not only the parameters of the controlling components and semantic parsing component, but also the embeddings of the tables and query words can be learned from scratch.
Towards Neural Network-based Reasoning
For example, it improves the accuracy on Path Finding(10K) from 33. 4% [6] to over 98%.
A Deep Memory-based Architecture for Sequence-to-Sequence Learning
We propose DEEPMEMORY, a novel deep architecture for sequence-to-sequence learning, which performs the task through a series of nonlinear transformations from the representation of the input sequence (e. g., a Chinese sentence) to the final output sequence (e. g., translation to English).
Learning to Answer Questions From Image Using Convolutional Neural Network
We demonstrate the efficacy of our proposed model on the DAQUAR and COCO-QA datasets, which are two benchmark datasets for the image QA, with the performances significantly outperforming the state-of-the-art.
Multimodal Convolutional Neural Networks for Matching Image and Sentence
In this paper, we propose multimodal convolutional neural networks (m-CNNs) for matching image and sentence.
Ranked #16 on
Image Retrieval
on Flickr30K 1K test
Self-Adaptive Hierarchical Sentence Model
The ability to accurately model a sentence at varying stages (e. g., word-phrase-sentence) plays a central role in natural language processing.
Ranked #5 on
Subjectivity Analysis
on SUBJ
$gen$CNN: A Convolutional Architecture for Word Sequence Prediction
Different from previous work on neural network-based language modeling and generation (e. g., RNN or LSTM), we choose not to greedily summarize the history of words as a fixed length vector.
Convolutional Neural Network Architectures for Matching Natural Language Sentences
Semantic matching is of central importance to many natural language tasks \cite{bordes2014semantic, RetrievalQA}.
Ranked #3 on
Question Answering
on SemEvalCQA
Context-Dependent Translation Selection Using Convolutional Neural Network
We propose a novel method for translation selection in statistical machine translation, in which a convolutional neural network is employed to judge the similarity between a phrase pair in two languages.
Syntax-based Deep Matching of Short Texts
Many tasks in natural language processing, ranging from machine translation to question answering, can be reduced to the problem of matching two sentences or more generally two short texts.
Neural Responding Machine for Short-Text Conversation
We propose Neural Responding Machine (NRM), a neural network-based response generator for Short-Text Conversation.
Encoding Source Language with Convolutional Neural Network for Machine Translation
The recently proposed neural network joint model (NNJM) (Devlin et al., 2014) augments the n-gram target language model with a heuristically chosen source context window, achieving state-of-the-art performance in SMT.
A Parallel and Efficient Algorithm for Learning to Match
In this paper, we tackle this challenge with a novel parallel and efficient algorithm for feature-based matrix factorization.
An Information Retrieval Approach to Short Text Conversation
Human computer conversation is regarded as one of the most difficult problems in artificial intelligence.
A Deep Architecture for Matching Short Texts
Many machine learning problems can be interpreted as learning for matching two types of objects (e. g., images and captions, users and products, queries and documents).
A Denoising View of Matrix Completion
In matrix completion, we are given a matrix where the values of only some of the entries are present, and we want to reconstruct the missing ones.
Hierarchical Fisher Kernels for Longitudinal Data
We develop new techniques for time series classification based on hierarchical Bayesian generative models (called mixed-effect models) and the Fisher kernel derived from them.
People Tracking with the Laplacian Eigenmaps Latent Variable Model
Reliably recovering 3D human pose from monocular video requires constraints that bias the estimates towards typical human poses and motions.
