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Found 147 papers, 43 papers with code
DuSQL: A Large-Scale and Pragmatic Chinese Text-to-SQL Dataset
This paper describes in detail the construction process and data statistics of DuSQL.
Why Don't You Click: Neural Correlates of Non-Click Behaviors in Web Search
Inspired by these findings, we conduct supervised learning tasks to estimate the usefulness of non-click results with brain signals and conventional information (i. e., content and context factors).
Physics-informed Neural Network for Nonlinear Dynamics in Fiber Optics
A physics-informed neural network (PINN) that combines deep learning with physics is studied to solve the nonlinear Schr\"odinger equation for learning nonlinear dynamics in fiber optics.
The HW-TSC's Offline Speech Translation Systems for IWSLT 2021 Evaluation
This paper describes our work in participation of the IWSLT-2021 offline speech translation task.
Understanding Human Reading Comprehension with Brain Signals
Reading comprehension is a complex cognitive process involving many human brain activities.
Jointly Optimizing Query Encoder and Product Quantization to Improve Retrieval Performance
Compared with previous DR models that use brute-force search, JPQ almost matches the best retrieval performance with 30x compression on index size.
Breaking the Corpus Bottleneck for Context-Aware Neural Machine Translation with Cross-Task Pre-training
To this end, we propose two pre-training tasks.
XLPT-AMR: Cross-Lingual Pre-Training via Multi-Task Learning for Zero-Shot AMR Parsing and Text Generation
We hope that knowledge gained while learning for English AMR parsing and text generation can be transferred to the counterparts of other languages.
Exploiting Rich Syntax for Better Knowledge Base Question Answering
Previous works for encoding questions mainly focus on the word sequences, but seldom consider the information from syntactic trees. In this paper, we propose an approach to learn syntax-based representations for KBQA.
R-Drop: Regularized Dropout for Neural Networks
Dropout is a powerful and widely used technique to regularize the training of deep neural networks.
Ranked #2 on
Machine Translation
on IWSLT2014 German-English
Learning on Abstract Domains: A New Approach for Verifiable Guarantee in Reinforcement Learning
Formally verifying Deep Reinforcement Learning (DRL) systems is a challenging task due to the dynamic continuity of system behaviors and the black-box feature of embedded neural networks.
A Large-Scale Rich Context Query and Recommendation Dataset in Online Knowledge-Sharing
To the best of our knowledge, this is the largest real-world interaction dataset for personalized recommendation.
Bridging Subword Gaps in Pretrain-Finetune Paradigm for Natural Language Generation
A well-known limitation in pretrain-finetune paradigm lies in its inflexibility caused by the one-size-fits-all vocabulary.
Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction
Bilingual Lexicon Induction (BLI) aims to map words in one language to their translations in another, and is typically through learning linear projections to align monolingual word representation spaces.
A Unified Span-Based Approach for Opinion Mining with Syntactic Constituents
Fine-grained opinion mining (OM) has achieved increasing attraction in the natural language processing (NLP) community, which aims to find the opinion structures of {``}Who expressed what opinions towards what{''} in one sentence.
An In-depth Study on Internal Structure of Chinese Words
Several previous works on syntactic parsing propose to annotate shallow word-internal structures for better utilizing character-level information.
Optimizing Dense Retrieval Model Training with Hard Negatives
ADORE replaces the widely-adopted static hard negative sampling method with a dynamic one to directly optimize the ranking performance.
Not All Attention Is All You Need
Beyond the success story of pre-trained language models (PrLMs) in recent natural language processing, they are susceptible to over-fitting due to unusual large model size.
Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots
To fill the gap between these up-to-date methods and the real-world applications, we incorporate user-specific dialogue history into the response selection and propose a personalized hybrid matching network (PHMN).
R-PointHop: A Green, Accurate and Unsupervised Point Cloud Registration Method
The use of LRF allows for hierarchical features of points to be invariant with respect to rotation and translation, thus making R-PointHop more robust in building point correspondence even when rotation angles are large.
Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing
Data augmentation has attracted a lot of research attention in the deep learning era for its ability in alleviating data sparseness.
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to its effective model architecture and the advantage of large-scale unlabeled scanned/digital-born documents.
Ranked #1 on
Document Image Classification
on RVL-CDIP
Code Summarization with Structure-induced Transformer
Code summarization (CS) is becoming a promising area in recent language understanding, which aims to generate sensible human language automatically for programming language in the format of source code, serving in the most convenience of programmer developing.
THUIR@COLIEE-2020: Leveraging Semantic Understanding and Exact Matching for Legal Case Retrieval and Entailment
We participated in the two case law tasks, i. e., the legal case retrieval task and the legal case entailment task.
Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition
We tackle implicit discourse relation recognition.
Semantic Role Labeling with Heterogeneous Syntactic Knowledge
Recently, due to the interplay between syntax and semantics, incorporating syntactic knowledge into neural semantic role labeling (SRL) has achieved much attention.
Multi-grained Chinese Word Segmentation with Weakly Labeled Data
Detailed evaluation shows that our proposed model with weakly labeled data significantly outperforms the state-of-the-art MWS model by 1. 12 and 5. 97 on NEWS and BAIKE data in F1.
Improving Relation Extraction with Relational Paraphrase Sentences
In this paper, we propose an alternative approach to improve RE systems via enriching diverse expressions by relational paraphrase sentences.
Semi-supervised Domain Adaptation for Dependency Parsing via Improved Contextualized Word Representations
The major challenge for current parsing research is to improve parsing performance on out-of-domain texts that are very different from the in-domain training data when there is only a small-scale out-domain labeled data.
Multi-Turn Dialogue Generation in E-Commerce Platform with the Context of Historical Dialogue
As an important research topic, customer service dialogue generation tends to generate generic seller responses by leveraging current dialogue information.
Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction
The experimental results show that the ranking lists of the comparison systems on the DS-labelled test data and human-annotated test data are different.
Token Drop mechanism for Neural Machine Translation
Neural machine translation with millions of parameters is vulnerable to unfamiliar inputs.
Learning To Retrieve: How to Train a Dense Retrieval Model Effectively and Efficiently
Through this process, it teaches the DR model how to retrieve relevant documents from the entire corpus instead of how to rerank a potentially biased sample of documents.
Tight Lower Complexity Bounds for Strongly Convex Finite-Sum Optimization
Finite-sum optimization plays an important role in the area of machine learning, and hence has triggered a surge of interest in recent years.
Improving AMR Parsing with Sequence-to-Sequence Pre-training
In the literature, the research on abstract meaning representation (AMR) parsing is much restricted by the size of human-curated dataset which is critical to build an AMR parser with good performance.
Ranked #2 on
AMR Parsing
on LDC2017T10
A new dataset of dog breed images and a benchmark for fine-grained classification
It is currently the largest dataset for fine-grained classification of dogs, including130 dog breeds and 70, 428 real-world images.
Attributes-Guided and Pure-Visual Attention Alignment for Few-Shot Recognition
However, these approaches only augment the representations of samples with available semantics while ignoring the query set, which loses the potential for the improvement and may lead to a shift between the modalities combination and the pure-visual representation.
Unsupervised Feedforward Feature (UFF) Learning for Point Cloud Classification and Segmentation
The UFF method exploits statistical correlations of points in a point cloud set to learn shape and point features in a one-pass feedforward manner through a cascaded encoder-decoder architecture.
Unsupervised Point Cloud Registration via Salient Points Analysis (SPA)
An unsupervised point cloud registration method, called salient points analysis (SPA), is proposed in this work.
Neural Logic Reasoning
Both reasoning and generalization ability are important for prediction tasks such as recommender systems, where reasoning provides strong connection between user history and target items for accurate prediction, and generalization helps the model to draw a robust user portrait over noisy inputs.
Accelerating Robustness Verification of Deep Neural Networks Guided by Target Labels
The key insight of our approach is that the robustness verification problem of DNNs can be solved by verifying sub-problems of DNNs, one per target label.
Train Your Data Processor: Distribution-Aware and Error-Compensation Coordinate Decoding for Human Pose Estimation
Recently, the leading performance of human pose estimation is dominated by heatmap based methods.
Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences
In this paper, we propose a new task of machine translation (MT), which is based on no parallel sentences but can refer to a ground-truth bilingual dictionary.
Aspect Sentiment Classification with Document-level Sentiment Preference Modeling
This justifies the importance of the document-level sentiment preference information to ASC and the effectiveness of our approach capturing such information.
Syntax-Aware Opinion Role Labeling with Dependency Graph Convolutional Networks
The experimental results show that syntactic information is highly valuable for ORL, and our final MTL model effectively boosts the F1 score by 9. 29 over the syntax-agnostic baseline.
Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation
However, existing KG enhanced recommendation methods have largely focused on exploring advanced neural network architectures to better investigate the structural information of KG.
Ranked #1 on
Recommendation Systems
on Amazon-Book
RepBERT: Contextualized Text Embeddings for First-Stage Retrieval
Although exact term match between queries and documents is the dominant method to perform first-stage retrieval, we propose a different approach, called RepBERT, to represent documents and queries with fixed-length contextualized embeddings.
Efficient Second-Order TreeCRF for Neural Dependency Parsing
Experiments and analysis on 27 datasets from 13 languages clearly show that techniques developed before the DL era, such as structural learning (global TreeCRF loss) and high-order modeling are still useful, and can further boost parsing performance over the state-of-the-art biaffine parser, especially for partially annotated training data.
Ranked #1 on
Dependency Parsing
on CoNLL-2009
Fully-Corrective Gradient Boosting with Squared Hinge: Fast Learning Rates and Early Stopping
Boosting is a well-known method for improving the accuracy of weak learners in machine learning.
Is POS Tagging Necessary or Even Helpful for Neural Dependency Parsing?
In the pre deep learning era, part-of-speech tags have been considered as indispensable ingredients for feature engineering in dependency parsing.
PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification
The PointHop method was recently proposed by Zhang et al. for 3D point cloud classification with unsupervised feature extraction.
Stochastic Item Descent Method for Large Scale Equal Circle Packing Problem
Inspired by its success in deep learning, we apply the idea of SGD with batch selection of samples to a classic optimization problem in decision version.
AE-OT-GAN: Training GANs from data specific latent distribution
Though generative adversarial networks (GANs) areprominent models to generate realistic and crisp images, they often encounter the mode collapse problems and arehard to train, which comes from approximating the intrinsicdiscontinuous distribution transform map with continuousDNNs.
Knowledge Graph Embedding via Graph Attenuated Attention Networks
However, these methods assign the same weights on the relation path in the knowledge graph and ignore the rich information presented in neighbor nodes, which result in incomplete mining of triple features.
Ranked #1 on
Link Prediction
on FB15k-237
Cross-lingual Pre-training Based Transfer for Zero-shot Neural Machine Translation
However, existing transfer methods involving a common target language are far from success in the extreme scenario of zero-shot translation, due to the language space mismatch problem between transferor (the parent model) and transferee (the child model) on the source side.
Improving Neural Relation Extraction with Positive and Unlabeled Learning
We present a novel approach to improve the performance of distant supervision relation extraction with Positive and Unlabeled (PU) Learning.
Robust Triple-Matrix-Recovery-Based Auto-Weighted Label Propagation for Classification
Our method can jointly re-cover the underlying clean data, clean labels and clean weighting spaces by decomposing the original data, predicted soft labels or weights into a clean part plus an error part by fitting noise.
A Syntax-aware Multi-task Learning Framework for Chinese Semantic Role Labeling
In this paper, we adopt a simple unified span-based model for both span-based and word-based Chinese SRL as a strong baseline.
SUDA-Alibaba at MRP 2019: Graph-Based Models with BERT
Our final submission ranks the third on the overall MRP evaluation metric, the first on EDS and the second on UCCA.
Contrastive Attention Mechanism for Abstractive Sentence Summarization
We propose a contrastive attention mechanism to extend the sequence-to-sequence framework for abstractive sentence summarization task, which aims to generate a brief summary of a given source sentence.
Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning
This approach incorporates clause selection and word selection strategies to tackle the data noise problem in the task of DASC.
Neural Logic Networks
The fundamental idea behind the design of most neural networks is to learn similarity patterns from data for prediction and inference, which lacks the ability of logical reasoning.
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation
Recent studies on AMR-to-text generation often formalize the task as a sequence-to-sequence (seq2seq) learning problem by converting an Abstract Meaning Representation (AMR) graph into a word sequence.
CCKS 2019 Shared Task on Inter-Personal Relationship Extraction
In this paper, we present the task definition, the description of data and the evaluation methodology used during this shared task.
Emotion Detection with Neural Personal Discrimination
Accordingly, we propose a Neural Personal Discrimination (NPD) approach to address above challenges by determining personal attributes from posts, and connecting relevant posts with similar attributes to jointly learn their emotions.
IPRE: a Dataset for Inter-Personal Relationship Extraction
Our data is the first dataset for inter-personal relationship extraction.
PointHop: An Explainable Machine Learning Method for Point Cloud Classification
In the attribute building stage, we address the problem of unordered point cloud data using a space partitioning procedure and developing a robust descriptor that characterizes the relationship between a point and its one-hop neighbor in a PointHop unit.
Syntax-aware Neural Semantic Role Labeling
Semantic role labeling (SRL), also known as shallow semantic parsing, is an important yet challenging task in NLP.
Sentence-Level Agreement for Neural Machine Translation
The training objective of neural machine translation (NMT) is to minimize the loss between the words in the translated sentences and those in the references.
Semi-supervised Domain Adaptation for Dependency Parsing
During the past decades, due to the lack of sufficient labeled data, most studies on cross-domain parsing focus on unsupervised domain adaptation, assuming there is no target-domain training data.
Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network
This paper extends the research to interactive reviews and proposes a new research task, namely Aspect Sentiment Classification towards Question-Answering (ASC-QA), for real-world applications.
Zero-Shot Cross-Lingual Abstractive Sentence Summarization through Teaching Generation and Attention
But there is no cross-lingual parallel corpus, whose source sentence language is different to the summary language, to directly train a cross-lingual ASSUM system.
HLT@SUDA at SemEval-2019 Task 1: UCCA Graph Parsing as Constituent Tree Parsing
The key idea is to convert a UCCA semantic graph into a constituent tree, in which extra labels are deliberately designed to mark remote edges and discontinuous nodes for future recovery.
Ranked #1 on
UCCA Parsing
on SemEval 2019 Task 1
Jointly Learning Structured Analysis Discriminative Dictionary and Analysis Multiclass Classifier
Then we compute a linear classifier based on the approximated sparse codes by an analysis mechanism to simultaneously consider the classification and representation powers.
Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations
Syntax has been demonstrated highly effective in neural machine translation (NMT).
Ranked #7 on
Machine Translation
on IWSLT2015 English-Vietnamese
Code-Switching for Enhancing NMT with Pre-Specified Translation
Leveraging user-provided translation to constrain NMT has practical significance.
HLT@SUDA at SemEval 2019 Task 1: UCCA Graph Parsing as Constituent Tree Parsing
The key idea is to convert a UCCA semantic graph into a constituent tree, in which extra labels are deliberately designed to mark remote edges and discontinuous nodes for future recovery.
Jointly Learning Explainable Rules for Recommendation with Knowledge Graph
The framework encourages two modules to complement each other in generating effective and explainable recommendation: 1) inductive rules, mined from item-centric knowledge graphs, summarize common multi-hop relational patterns for inferring different item associations and provide human-readable explanation for model prediction; 2) recommendation module can be augmented by induced rules and thus have better generalization ability dealing with the cold-start issue.
A Model Combining Convolutional Neural Network and LightGBM Algorithm for Ultra-Short-Term Wind Power Forecasting
In this paper, a new forecasting model based on a convolution neural network and LightGBM is constructed.
A Novel Euler's Elastica based Segmentation Approach for Noisy Images via using the Progressive Hedging Algorithm
Euler's Elastica based unsupervised segmentation models have strong capability of completing the missing boundaries for existing objects in a clean image, but they are not working well for noisy images.
Semi-supervised learning via Feedforward-Designed Convolutional Neural Networks
A semi-supervised learning framework using the feedforward-designed convolutional neural networks (FF-CNNs) is proposed for image classification in this work.
Improving the Transformer Translation Model with Document-Level Context
Although the Transformer translation model (Vaswani et al., 2017) has achieved state-of-the-art performance in a variety of translation tasks, how to use document-level context to deal with discourse phenomena problematic for Transformer still remains a challenge.
Interpretable Convolutional Neural Networks via Feedforward Design
To construct convolutional layers, we develop a new signal transform, called the Saab (Subspace Approximation with Adjusted Bias) transform.
Using active learning to expand training data for implicit discourse relation recognition
We tackle discourse-level relation recognition, a problem of determining semantic relations between text spans.
Sentiment Classification towards Question-Answering with Hierarchical Matching Network
On the basis, we propose a three-stage hierarchical matching network to explore deep sentiment information in a QA text pair.
Adaptive Weighting for Neural Machine Translation
In the popular sequence to sequence (seq2seq) neural machine translation (NMT), there exist many weighted sum models (WSMs), each of which takes a set of input and generates one output.
Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning
A bottleneck problem with Chinese named entity recognition (NER) in new domains is the lack of annotated data.
One vs. Many QA Matching with both Word-level and Sentence-level Attention Network
Question-Answer (QA) matching is a fundamental task in the Natural Language Processing community.
LineNet: a Zoomable CNN for Crowdsourced High Definition Maps Modeling in Urban Environments
Combining LineNet and TTLane, we proposed a pipeline to model HD maps with crowdsourced data for the first time.
Report of NEWS 2018 Named Entity Transliteration Shared Task
This report presents the results from the Named Entity Transliteration Shared Task conducted as part of The Seventh Named Entities Workshop (NEWS 2018) held at ACL 2018 in Melbourne, Australia.
Supervised Treebank Conversion: Data and Approaches
Treebank conversion is a straightforward and effective way to exploit various heterogeneous treebanks for boosting parsing performance.
Classification of lung nodules in CT images based on Wasserstein distance in differential geometry
The Wasserstein distance between the nodules is calculated based on our new spherical optimal mass transport, this new algorithm works directly on sphere by using spherical metric, which is much more accurate and efficient than previous methods.
Aspect Sentiment Classification with both Word-level and Clause-level AttentionNetworks
Aspect sentiment classification, a challenging taskin sentiment analysis, has been attracting more andmore attention in recent years.
Adversarial Learning for Chinese NER from Crowd Annotations
To quickly obtain new labeled data, we can choose crowdsourcing as an alternative way at lower cost in a short time.
SEE: Syntax-aware Entity Embedding for Neural Relation Extraction
First, we encode the context of entities on a dependency tree as sentence-level entity embedding based on tree-GRU.
Improved English to Russian Translation by Neural Suffix Prediction
Neural machine translation (NMT) suffers a performance deficiency when a limited vocabulary fails to cover the source or target side adequately, which happens frequently when dealing with morphologically rich languages.
Dependency Parsing with Partial Annotations: An Empirical Comparison
This paper describes and compares two straightforward approaches for dependency parsing with partial annotations (PA).
Multi-Grained Chinese Word Segmentation
Traditionally, word segmentation (WS) adopts the single-grained formalism, where a sentence corresponds to a single word sequence.
Translating Phrases in Neural Machine Translation
Otherwise, the NMT decoder generates a word from the vocabulary as the general NMT decoder does.
Modeling Source Syntax for Neural Machine Translation
Even though a linguistics-free sequence to sequence model in neural machine translation (NMT) has certain capability of implicitly learning syntactic information of source sentences, this paper shows that source syntax can be explicitly incorporated into NMT effectively to provide further improvements.
Improving Statistical Machine Translation with Selectional Preferences
In this paper, we study semantic dependencies between verbs and their arguments by modeling selectional preferences in the context of machine translation.
Bilingual Autoencoders with Global Descriptors for Modeling Parallel Sentences
Parallel sentence representations are important for bilingual and cross-lingual tasks in natural language processing.
Learning Event Expressions via Bilingual Structure Projection
Aiming to resolve high complexities of event descriptions, previous work (Huang and Riloff, 2013) proposes multi-faceted event recognition and a bootstrapping method to automatically acquire both event facet phrases and event expressions from unannotated texts.
Distributed Representations for Building Profiles of Users and Items from Text Reviews
In this paper, we propose an approach to learn distributed representations of users and items from text comments for recommendation systems.
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.
Training Dependency Parsers with Partial Annotation
The first approach is previously proposed to directly train a log-linear graph-based parser (LLGPar) with PA based on a forest-based objective.
Word Segmentation on Micro-blog Texts with External Lexicon and Heterogeneous Data
This paper describes our system designed for the NLPCC 2016 shared task on word segmentation on micro-blog texts.
Variational Neural Machine Translation
Models of neural machine translation are often from a discriminative family of encoderdecoders that learn a conditional distribution of a target sentence given a source sentence.
Variational Neural Discourse Relation Recognizer
In order to perform efficient inference and learning, we introduce neural discourse relation models to approximate the prior and posterior distributions of the latent variable, and employ these approximated distributions to optimize a reparameterized variational lower bound.
Boost Phrase-level Polarity Labelling with Review-level Sentiment Classification
In this paper, we focus on the problem of phrase-level sentiment polarity labelling and attempt to bridge the gap between phrase-level and review-level sentiment analysis.
Minimum $n$-Rank Approximation via Iterative Hard Thresholding
Additionally, combining an effective heuristic for determining $n$-rank, we can also apply the proposed algorithm to solve MnRA when $n$-rank is unknown in advance.
