Search Results for author:
Found 66 papers, 28 papers with code
An Empirical Study on the Language Modal in Visual Question Answering
Generalization beyond in-domain experience to out-of-distribution data is of paramount significance in the AI domain.
Measuring Cross-Lingual Transferability of Multilingual Transformers on Sentence Classification
Recent studies have exhibited remarkable capabilities of pre-trained multilingual Transformers, especially cross-lingual transferability.
Towards Hierarchical Policy Learning for Conversational Recommendation with Hypergraph-based Reinforcement Learning
Conversational recommendation systems (CRS) aim to timely and proactively acquire user dynamic preferred attributes through conversations for item recommendation.
AttenWalker: Unsupervised Long-Document Question Answering via Attention-based Graph Walking
To alleviate the problem, it might be possible to generate long-document QA pairs via unsupervised question answering (UQA) methods.
Gated Mechanism Enhanced Multi-Task Learning for Dialog Routing
Although most existing methods can fulfil this requirement, they can only model single-source dialog data and cannot effectively capture the underlying knowledge of relations among data and subtasks.
Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension
Open-retrieval conversational machine reading comprehension (OCMRC) simulates real-life conversational interaction scenes.
Momentum Decoding: Open-ended Text Generation As Graph Exploration
In this study, we formulate open-ended text generation from a new perspective, i. e., we view it as an exploration process within a directed graph.
STAGE: Span Tagging and Greedy Inference Scheme for Aspect Sentiment Triplet Extraction
Hence, we propose a novel approach, Span TAgging and Greedy infErence (STAGE), to extract sentiment triplets in span-level, where each span may consist of multiple words and play different roles simultaneously.
Sequential Topic Selection Model with Latent Variable for Topic-Grounded Dialogue
To address the problem, in this paper we propose a novel approach, named Sequential Global Topic Attention (SGTA) to exploit topic transition over all conversations in a subtle way for better modeling post-to-response topic-transition and guiding the response generation to the current conversation.
HCL-TAT: A Hybrid Contrastive Learning Method for Few-shot Event Detection with Task-Adaptive Threshold
Conventional event detection models under supervised learning settings suffer from the inability of transfer to newly-emerged event types owing to lack of sufficient annotations.
Capturing Global Structural Information in Long Document Question Answering with Compressive Graph Selector Network
The proposed model mainly focuses on the evidence selection phase of long document question answering.
ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension
Conversational machine reading comprehension (CMRC) aims to assist computers to understand an natural language text and thereafter engage in a multi-turn conversation to answer questions related to the text.
Unsupervised Hashing with Semantic Concept Mining
Recently, to improve the unsupervised image retrieval performance, plenty of unsupervised hashing methods have been proposed by designing a semantic similarity matrix, which is based on the similarities between image features extracted by a pre-trained CNN model.
Multi-level Contrastive Learning Framework for Sequential Recommendation
Different from the previous contrastive learning-based methods for SR, MCLSR learns the representations of users and items through a cross-view contrastive learning paradigm from four specific views at two different levels (i. e., interest- and feature-level).
Unsupervised Question Answering via Answer Diversifying
Previous works usually make use of heuristic rules as well as pre-trained models to construct data and train QA models.
Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning
Specifically, we first construct local and non-local graphs for user/item in KG, exploring more KG facts for KGR.
Person-job fit estimation from candidate profile and related recruitment history with co-attention neural networks
Existing studies on person-job fit, however, mainly focus on calculating the similarity between the candidate resumes and the job postings on the basis of their contents, without taking the recruiters' experience (i. e., historical successful recruitment records) into consideration.
Relational Triple Extraction: One Step is Enough
Extracting relational triples from unstructured text is an essential task in natural language processing and knowledge graph construction.
On the Representation Collapse of Sparse Mixture of Experts
We also present a comprehensive analysis on the representation and routing behaviors of our models.
Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System
Different from traditional contrastive learning methods which generate two graph views by uniform data augmentation schemes such as corruption or dropping, we comprehensively consider three different graph views for KG-aware recommendation, including global-level structural view, local-level collaborative and semantic views.
Cross-Lingual Phrase Retrieval
In this paper, we propose XPR, a cross-lingual phrase retriever that extracts phrase representations from unlabeled example sentences.
BiSyn-GAT+: Bi-Syntax Aware Graph Attention Network for Aspect-based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that aims to align aspects and corresponding sentiments for aspect-specific sentiment polarity inference.
Hammer PDF: An Intelligent PDF Reader for Scientific Papers
Specifically, taking a paper as a basic and separate unit, existing PDF Readers cannot access extended information about the paper, such as corresponding videos, blogs and codes.
OneRel:Joint Entity and Relation Extraction with One Module in One Step
Joint entity and relation extraction is an essential task in natural language processing and knowledge graph construction.
Exploring Dense Retrieval for Dialogue Response Selection
In this study, we present a solution to directly select proper responses from a large corpus or even a nonparallel corpus that only consists of unpaired sentences, using a dense retrieval model.
Cross-Lingual Language Model Meta-Pretraining
The success of pretrained cross-lingual language models relies on two essential abilities, i. e., generalization ability for learning downstream tasks in a source language, and cross-lingual transferability for transferring the task knowledge to other languages.
Context-aware Entity Typing in Knowledge Graphs
Knowledge graph entity typing aims to infer entities' missing types in knowledge graphs which is an important but under-explored issue.
XLM-E: Cross-lingual Language Model Pre-training via ELECTRA
In this paper, we introduce ELECTRA-style tasks to cross-lingual language model pre-training.
Ranked #1 on
Zero-Shot Cross-Lingual Transfer
on XTREME
Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment
The cross-lingual language models are typically pretrained with masked language modeling on multilingual text or parallel sentences.
Global Context Enhanced Graph Neural Networks for Session-based Recommendation
In GCE-GNN, we propose a novel global-level item representation learning layer, which employs a session-aware attention mechanism to recursively incorporate the neighbors' embeddings of each node on the global graph.
Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking
Chinese Spell Checking (CSC) aims to detect and correct erroneous characters for user-generated text in the Chinese language.
Ranked #2 on
Chinese Spell Checking
on SIGHAN 2015
Comprehensive Study: How the Context Information of Different Granularity Affects Dialogue State Tracking?
Thus, in this paper, we will study and discuss how the context information of different granularity affects dialogue state tracking.
A Robust and Domain-Adaptive Approach for Low-Resource Named Entity Recognition
Recently, it has attracted much attention to build reliable named entity recognition (NER) systems using limited annotated data.
Ranked #3 on
Named Entity Recognition (NER)
on SciERC
(using extra training data)
Low Resource Named Entity Recognition
named-entity-recognition
+2
Self-attention Comparison Module for Boosting Performance on Retrieval-based Open-Domain Dialog Systems
Since the pre-trained language models are widely used, retrieval-based open-domain dialog systems, have attracted considerable attention from researchers recently.
Ultra-Fast, Low-Storage, Highly Effective Coarse-grained Selection in Retrieval-based Chatbot by Using Deep Semantic Hashing
Specifically, in our proposed DSHC model, a hashing optimizing module that consists of two autoencoder models is stacked on a trained dense representation model, and three loss functions are designed to optimize it.
Exploiting Group-level Behavior Pattern forSession-based Recommendation
In RNMSR, we propose to learn the user preference from both instance-level and group-level, respectively: (i) instance-level, which employs GNNs on a similarity-based item-pairwise session graph to capture the users' preference in instance-level.
Exploring Global Information for Session-based Recommendation
Based on BGNN, we propose a novel approach, called Session-based Recommendation with Global Information (SRGI), which infers the user preferences via fully exploring global item-transitions over all sessions from two different perspectives: (i) Fusion-based Model (SRGI-FM), which recursively incorporates the neighbor embeddings of each node on global graph into the learning process of session level item representation; and (ii) Constrained-based Model (SRGI-CM), which treats the global-level item-transition information as a constraint to ensure the learned item embeddings are consistent with the global item-transition.
User-based Network Embedding for Collective Opinion Spammer Detection
Due to the huge commercial interests behind online reviews, a tremendousamount of spammers manufacture spam reviews for product reputation manipulation.
Deep Cross-modal Hashing via Margin-dynamic-softmax Loss
Finally, by minimizing the novel \textit{margin-dynamic-softmax loss}, the modality-specific hashing networks can be trained to generate hash codes which can simultaneously preserve the cross-modal similarity and abundant semantic information well.
Deep Kernel Supervised Hashing for Node Classification in Structural Networks
However, nearly all the existing network embedding based methods are hard to capture the actual category features of a node because of the linearly inseparable problem in low-dimensional space; meanwhile they cannot incorporate simultaneously network structure information and node label information into network embedding.
Which Kind Is Better in Open-domain Multi-turn Dialog,Hierarchical or Non-hierarchical Models? An Empirical Study
Thus, in this paper, we will measure systematically nearly all representative hierarchical and non-hierarchical models over the same experimental settings to check which kind is better.
InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training
In this work, we present an information-theoretic framework that formulates cross-lingual language model pre-training as maximizing mutual information between multilingual-multi-granularity texts.
Ranked #16 on
Zero-Shot Cross-Lingual Transfer
on XTREME
Generating Informative Dialogue Responses with Keywords-Guided Networks
Specifically, KW-Seq2Seq first uses a keywords decoder to predict some topic keywords, and then generates the final response under the guidance of them.
Weibo-COV: A Large-Scale COVID-19 Social Media Dataset from Weibo
Therefore, in this paper, we release Weibo-COV, a first large-scale COVID-19 social media dataset from Weibo, covering more than 30 million tweets from 1 November 2019 to 30 April 2020.
Social and Information Networks
Context-Sensitive Generation Network for Handing Unknown Slot Values in Dialogue State Tracking
As a key component in a dialogue system, dialogue state tracking plays an important role.
Learning Relation Ties with a Force-Directed Graph in Distant Supervised Relation Extraction
Then, we borrow the idea of Coulomb's Law from physics and introduce the concept of attractive force and repulsive force to this graph to learn correlation and mutual exclusion between relations.
PONE: A Novel Automatic Evaluation Metric for Open-Domain Generative Dialogue Systems
Through extensive experiments, the learning-based metrics are demonstrated that they are the most effective evaluation metrics for open-domain generative dialogue systems.
When to Talk: Chatbot Controls the Timing of Talking during Multi-turn Open-domain Dialogue Generation
Intuitively, a dialogue model that can control the timing of talking autonomously based on the conversation context can chat with humans more naturally.
DialogAct2Vec: Towards End-to-End Dialogue Agent by Multi-Task Representation Learning
In this paper, we propose a novel joint end-to-end model by multi-task representation learning, which can capture the knowledge from heterogeneous information through automatically learning knowledgeable low-dimensional embeddings from data, named with DialogAct2Vec.
Can Monolingual Pretrained Models Help Cross-Lingual Classification?
Multilingual pretrained language models (such as multilingual BERT) have achieved impressive results for cross-lingual transfer.
SEPT: Improving Scientific Named Entity Recognition with Span Representation
We introduce a new scientific named entity recognizer called SEPT, which stands for Span Extractor with Pre-trained Transformers.
Cross-Lingual Natural Language Generation via Pre-Training
In this work we focus on transferring supervision signals of natural language generation (NLG) tasks between multiple languages.
Generative Dialog Policy for Task-oriented Dialog Systems
As far as we know, the existing task-oriented dialogue systems obtain the dialogue policy through classification, which can assign either a dialogue act and its corresponding parameters or multiple dialogue acts without their corresponding parameters for a dialogue action.
Stack-VS: Stacked Visual-Semantic Attention for Image Caption Generation
Recently, automatic image caption generation has been an important focus of the work on multimodal translation task.
Multi-task Learning for Low-resource Second Language Acquisition Modeling
Second language acquisition (SLA) modeling is to predict whether second language learners could correctly answer the questions according to what they have learned.
Position-Aware Self-Attention based Neural Sequence Labeling
Sequence labeling is a fundamental task in natural language processing and has been widely studied.
Towards End-to-End Learning for Efficient Dialogue Agent by Modeling Looking-ahead Ability
Learning an efficient manager of dialogue agent from data with little manual intervention is important, especially for goal-oriented dialogues.
Complicated Table Structure Recognition
It also attracts lots of attention to recognize the table structures in PDF files.
Deep Hashing for Signed Social Network Embedding
Network embedding is a promising way of network representation, facilitating many signed social network processing and analysis tasks such as link prediction and node classification.
Deep Cross-Modal Hashing with Hashing Functions and Unified Hash Codes Jointly Learning
Specifically, by an iterative optimization algorithm, DCHUC jointly learns unified hash codes for image-text pairs in a database and a pair of hash functions for unseen query image-text pairs.
Object Detection based Deep Unsupervised Hashing
Recently, similarity-preserving hashing methods have been extensively studied for large-scale image retrieval.
Supervised Deep Hashing for Hierarchical Labeled Data
Specifically, we define a novel similarity formula for hierarchical labeled data by weighting each layer, and design a deep convolutional neural network to obtain a hash code for each data point.
Conceptualization Topic Modeling
Recently, topic modeling has been widely used to discover the abstract topics in text corpora.
A Novel Fast Framework for Topic Labeling Based on Similarity-preserved Hashing
Recently, topic modeling has been widely applied in data mining due to its powerful ability.
