Search Results for author:
Found 35 papers, 10 papers with code
A Structure-aware Generative Model for Biomedical Event Extraction
In this paper, we propose an event structure-aware generative model named GenBEE, which can capture complex event structures in biomedical text for biomedical event extraction.
A Multimodal Data-driven Framework for Anxiety Screening
Multimodal data can provide more objective evidence for anxiety screening to improve the accuracy of models.
Dialogue State Distillation Network with Inter-slot Contrastive Learning for Dialogue State Tracking
In this paper, we propose a Dialogue State Distillation Network (DSDN) to utilize relevant information of previous dialogue states and migrate the gap of utilization between training and testing.
Semantic Pivoting Model for Effective Event Detection
Event Detection, which aims to identify and classify mentions of event instances from unstructured articles, is an important task in Natural Language Processing (NLP).
Machine Learning for Food Review and Recommendation
In this paper, we use different deep learning approaches to address the problems of sentiment analysis, automatic review tag generation, and retrieval of food reviews.
Modularized Interaction Network for Named Entity Recognition
Although the existing Named Entity Recognition (NER) models have achieved promising performance, they suffer from certain drawbacks.
Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with $1/n$ Parameters
Recent works have demonstrated reasonable success of representation learning in hypercomplex space.
Compositional De-Attention Networks
Attentional models are distinctly characterized by their ability to learn relative importance, i. e., assigning a different weight to input values.
R2D2: Reuse & Reduce via Dynamic Weight Diffusion for Training Efficient NLP Models
We propose R2D2 layers, a new neural block for training efficient NLP models.
Holographic Factorization Machines for Recommendation
Factorization Machines (FMs) are a class of popular algorithms that have been widely adopted for collaborative filtering and recommendation tasks.
Collecting and Analyzing Multidimensional Data with Local Differential Privacy
Motivated by this, we first propose novel LDP mechanisms for collecting a numeric attribute, whose accuracy is at least no worse (and usually better) than existing solutions in terms of worst-case noise variance.
Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks
Many state-of-the-art neural models for NLP are heavily parameterized and thus memory inefficient.
Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives
This paper tackles the problem of reading comprehension over long narratives where documents easily span over thousands of tokens.
Recurrently Controlled Recurrent Networks
Recurrent neural networks (RNNs) such as long short-term memory and gated recurrent units are pivotal building blocks across a broad spectrum of sequence modeling problems.
Densely Connected Attention Propagation for Reading Comprehension
Secondly, the dense connectors in our network are learned via attention instead of standard residual skip-connectors.
Ranked #2 on
Question Answering
on NewsQA
Co-Stack Residual Affinity Networks with Multi-level Attention Refinement for Matching Text Sequences
This task enables many potential applications such as question answering and paraphrase identification.
Multi-Granular Sequence Encoding via Dilated Compositional Units for Reading Comprehension
Sequence encoders are crucial components in many neural architectures for learning to read and comprehend.
Ranked #7 on
Question Answering
on NarrativeQA
Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification
This paper proposes a new neural architecture that exploits readily available sentiment lexicon resources.
Self-Attentive Neural Collaborative Filtering
This paper has been withdrawn as we discovered a bug in our tensorflow implementation that involved accidental mixing of vectors across batches.
Multi-Cast Attention Networks for Retrieval-based Question Answering and Response Prediction
Attention is typically used to select informative sub-phrases that are used for prediction.
CoupleNet: Paying Attention to Couples with Coupled Attention for Relationship Recommendation
Our approach, the CoupleNet is an end-to-end deep learning based estimator that analyzes the social profiles of two users and subsequently performs a similarity match between the users.
Reasoning with Sarcasm by Reading In-between
Sarcasm is a sophisticated speech act which commonly manifests on social communities such as Twitter and Reddit.
Multi-range Reasoning for Machine Comprehension
Similarly, we achieve competitive performance relative to AMANDA on the SearchQA benchmark and BiDAF on the NarrativeQA benchmark without using any LSTM/GRU layers.
Ranked #5 on
Question Answering
on RACE
Multi-Pointer Co-Attention Networks for Recommendation
Our model operates on a multi-hierarchical paradigm and is based on the intuition that not all reviews are created equal, i. e., only a select few are important.
Compare, Compress and Propagate: Enhancing Neural Architectures with Alignment Factorization for Natural Language Inference
Firstly, we introduce a new architecture where alignment pairs are compared, compressed and then propagated to upper layers for enhanced representation learning.
Ranked #7 on
Natural Language Inference
on SciTail
Learning to Attend via Word-Aspect Associative Fusion for Aspect-based Sentiment Analysis
Our novel model, \textit{Aspect Fusion LSTM} (AF-LSTM) learns to attend based on associative relationships between sentence words and aspect which allows our model to adaptively focus on the correct words given an aspect term.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Cross Temporal Recurrent Networks for Ranking Question Answer Pairs
This paper explores the idea of learning temporal gates for sequence pairs (question and answer), jointly influencing the learned representations in a pairwise manner.
SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text Scoring
Our new method proposes a new \textsc{SkipFlow} mechanism that models relationships between snapshots of the hidden representations of a long short-term memory (LSTM) network as it reads.
Ranked #5 on
Automated Essay Scoring
on ASAP-AES
Differentially Private Regression for Discrete-Time Survival Analysis
However, for sensitive survival data such as medical data, there are serious concerns about the privacy of individuals in the data set when medical data is used to fit the regression models.
Multi-task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs
Unfortunately, many state-of-the-art relational learning models ignore this information due to the challenging nature of dealing with non-discrete data types in the inherently binary-natured knowledge graphs.
Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering
The dominant neural architectures in question answer retrieval are based on recurrent or convolutional encoders configured with complex word matching layers.
Ranked #1 on
Question Answering
on SemEvalCQA
Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking
Our model, LRML (\textit{Latent Relational Metric Learning}) is a novel metric learning approach for recommendation.
Ranked #1 on
Recommendation Systems
on Netflix
(nDCG@10 metric)
Collecting and Analyzing Data from Smart Device Users with Local Differential Privacy
Organizations with a large user base, such as Samsung and Google, can potentially benefit from collecting and mining users' data.
Databases
Utilizing Temporal Information for Taxonomy Construction
Taxonomies play an important role in many applications by organizing domain knowledge into a hierarchy of {`}is-a{'} relations between terms.
