Search Results for author:
Found 60 papers, 18 papers with code
Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product
We annotate a multimodal product attribute value dataset that contains 87, 194 instances, and the experimental results on this dataset demonstrate that explicitly modeling the relationship between attributes and values facilitates our method to establish the correspondence between them, and selectively utilizing visual product information is necessary for the task.
Self-Attention Guided Copy Mechanism for Abstractive Summarization
Copy module has been widely equipped in the recent abstractive summarization models, which facilitates the decoder to extract words from the source into the summary.
Speaker Diarization with Lexical Information
This work presents a novel approach for speaker diarization to leverage lexical information provided by automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Graph Sequential Network for Reasoning over Sequences
We validate the proposed GSN on two NLP tasks: interpretable multi-hop reading comprehension on HotpotQA and graph based fact verification on FEVER.
The JDDC Corpus: A Large-Scale Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service
Human conversations are complicated and building a human-like dialogue agent is an extremely challenging task.
Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding
Translational distance-based knowledge graph embedding has shown progressive improvements on the link prediction task, from TransE to the latest state-of-the-art RotatE.
Ranked #19 on
Link Prediction
on FB15k-237
Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documents
Interpretable multi-hop reading comprehension (RC) over multiple documents is a challenging problem because it demands reasoning over multiple information sources and explaining the answer prediction by providing supporting evidences.
Relation Module for Non-Answerable Predictions on Reading Comprehension
We test the relation module on the SQuAD 2. 0 dataset using both the BiDAF and BERT models as baseline readers.
Relation Module for Non-answerable Prediction on Question Answering
In this paper, we aim to improve a MRC model's ability to determine whether a question has an answer in a given context (e. g. the recently proposed SQuAD 2. 0 task).
Zero-shot Text-to-SQL Learning with Auxiliary Task
Recent years have seen great success in the use of neural seq2seq models on the text-to-SQL task.
Multiple instance learning with graph neural networks
In this paper, we propose a new end-to-end graph neural network (GNN) based algorithm for MIL: we treat each bag as a graph and use GNN to learn the bag embedding, in order to explore the useful structural information among instances in bags.
Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss
In this paper, we improve the robustness of DNNs by utilizing techniques of Distance Metric Learning.
Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs
We introduce a heterogeneous graph with different types of nodes and edges, which is named as Heterogeneous Document-Entity (HDE) graph.
Reliable Weakly Supervised Learning: Maximize Gain and Maintain Safeness
We guide the optimization of label quality through a small amount of validation data, and to ensure the safeness of performance while maximizing performance gain.
Deep Speaker Embedding Learning with Multi-Level Pooling for Text-Independent Speaker Verification
This paper aims to improve the widely used deep speaker embedding x-vector model.
Few-shot Learning with Meta Metric Learners
Our meta metric learning approach consists of task-specific learners, that exploit metric learning to handle flexible labels, and a meta learner, that discovers good parameters and gradient decent to specify the metrics in task-specific learners.
End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion
The recent graph convolutional network (GCN) provides another way of learning graph node embedding by successfully utilizing graph connectivity structure.
Ranked #30 on
Link Prediction
on FB15k-237
Universal Stagewise Learning for Non-Convex Problems with Convergence on Averaged Solutions
For example, there is still a lack of theories of convergence for SGD and its variants that use stagewise step size and return an averaged solution in practice.
Scheduled Policy Optimization for Natural Language Communication with Intelligent Agents
We investigate the task of learning to follow natural language instructions by jointly reasoning with visual observations and language inputs.
Diverse Few-Shot Text Classification with Multiple Metrics
We study few-shot learning in natural language domains.
Group Sparse CNNs for Question Classification with Answer Sets
Question classification is an important task with wide applications.
Jointly Trained Sequential Labeling and Classification by Sparse Attention Neural Networks
In order to utilize the potential benefits from their correlations, we propose a jointly trained model for learning the two tasks simultaneously via Long Short-Term Memory (LSTM) networks.
Learning to update Auto-associative Memory in Recurrent Neural Networks for Improving Sequence Memorization
Learning to remember long sequences remains a challenging task for recurrent neural networks.
R$^3$: Reinforced Reader-Ranker for Open-Domain Question Answering
Second, we propose a novel method that jointly trains the Ranker along with an answer-generation Reader model, based on reinforcement learning.
Ranked #4 on
Open-Domain Question Answering
on Quasar
Robust Task Clustering for Deep Many-Task Learning
We propose a new method to measure task similarities with cross-task transfer performance matrix for the deep learning scenario.
SenGen: Sentence Generating Neural Variational Topic Model
We present a new topic model that generates documents by sampling a topic for one whole sentence at a time, and generating the words in the sentence using an RNN decoder that is conditioned on the topic of the sentence.
Learning Loss Functions for Semi-supervised Learning via Discriminative Adversarial Networks
We propose discriminative adversarial networks (DAN) for semi-supervised learning and loss function learning.
Improved Neural Relation Detection for Knowledge Base Question Answering
Relation detection is a core component for many NLP applications including Knowledge Base Question Answering (KBQA).
A Structured Self-attentive Sentence Embedding
This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention.
Neural Models for Sequence Chunking
Many natural language understanding (NLU) tasks, such as shallow parsing (i. e., text chunking) and semantic slot filling, require the assignment of representative labels to the meaningful chunks in a sentence.
GaDei: On Scale-up Training As A Service For Deep Learning
By evaluating the NLC workloads, we show that only the conservative hyper-parameter setup (e. g., small mini-batch size and small learning rate) can guarantee acceptable model accuracy for a wide range of customers.
Classify or Select: Neural Architectures for Extractive Document Summarization
The Selector architecture, on the other hand, is free to pick one sentence at a time in any arbitrary order to piece together the summary.
SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents
We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence model for extractive summarization of documents and show that it achieves performance better than or comparable to state-of-the-art.
Ranked #8 on
Text Summarization
on CNN / Daily Mail (Anonymized)
End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension
This paper proposes dynamic chunk reader (DCR), an end-to-end neural reading comprehension (RC) model that is able to extract and rank a set of answer candidates from a given document to answer questions.
Ranked #49 on
Question Answering
on SQuAD1.1 dev
Simple Question Answering by Attentive Convolutional Neural Network
In fact selection, we match the subject entity in a fact candidate with the entity mention in the question by a character-level convolutional neural network (char-CNN), and match the predicate in that fact with the question by a word-level CNN (word-CNN).
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation
We introduce the multiresolution recurrent neural network, which extends the sequence-to-sequence framework to model natural language generation as two parallel discrete stochastic processes: a sequence of high-level coarse tokens, and a sequence of natural language tokens.
Ranked #1 on
Dialogue Generation
on Ubuntu Dialogue (Activity)
Pointing the Unknown Words
At each time-step, the decision of which softmax layer to use choose adaptively made by an MLP which is conditioned on the context.~We motivate our work from a psychological evidence that humans naturally have a tendency to point towards objects in the context or the environment when the name of an object is not known.~We observe improvements on two tasks, neural machine translation on the Europarl English to French parallel corpora and text summarization on the Gigaword dataset using our proposed model.
Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
In this work, we model abstractive text summarization using Attentional Encoder-Decoder Recurrent Neural Networks, and show that they achieve state-of-the-art performance on two different corpora.
Ranked #10 on
Text Summarization
on DUC 2004 Task 1
Attentive Pooling Networks
In this work, we propose Attentive Pooling (AP), a two-way attention mechanism for discriminative model training.
Ranked #2 on
Question Answering
on SemEvalCQA
Leveraging Sentence-level Information with Encoder LSTM for Semantic Slot Filling
Recurrent Neural Network (RNN) and one of its specific architectures, Long Short-Term Memory (LSTM), have been widely used for sequence labeling.
ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs
(ii) We propose three attention schemes that integrate mutual influence between sentences into CNN; thus, the representation of each sentence takes into consideration its counterpart.
Good, Better, Best: Choosing Word Embedding Context
We propose two methods of learning vector representations of words and phrases that each combine sentence context with structural features extracted from dependency trees.
LSTM-based Deep Learning Models for Non-factoid Answer Selection
One direction is to define a more composite representation for questions and answers by combining convolutional neural network with the basic framework.
Distributed Deep Learning for Question Answering
This paper is an empirical study of the distributed deep learning for question answering subtasks: answer selection and question classification.
Empirical Study on Deep Learning Models for Question Answering
In this paper we explore deep learning models with memory component or attention mechanism for question answering task.
Structured Memory for Neural Turing Machines
Neural Turing Machines (NTM) contain memory component that simulates "working memory" in the brain to store and retrieve information to ease simple algorithms learning.
Applying Deep Learning to Answer Selection: A Study and An Open Task
We apply a general deep learning framework to address the non-factoid question answering task.
Dependency-based Convolutional Neural Networks for Sentence Embedding
In sentence modeling and classification, convolutional neural network approaches have recently achieved state-of-the-art results, but all such efforts process word vectors sequentially and neglect long-distance dependencies.
Medical Synonym Extraction with Concept Space Models
In this paper, we present a novel approach for medical synonym extraction.
Classifying Relations by Ranking with Convolutional Neural Networks
Relation classification is an important semantic processing task for which state-ofthe-art systems still rely on costly handcrafted features.
Ranked #27 on
Relation Extraction
on SemEval-2010 Task-8
