Search Results for author:
Found 40 papers, 11 papers with code
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.
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.
News-Driven Stock Prediction With Attention-Based Noisy Recurrent State Transition
Thanks to the use of attention over news events, our model is also more explainable.
Evaluating Low-Resource Machine Translation between Chinese and Vietnamese with Back-Translation
Back translation (BT) has been widely used and become one of standard techniques for data augmentation in Neural Machine Translation (NMT), BT has proven to be helpful for improving the performance of translation effectively, especially for low-resource scenarios.
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.
Tag Recommendation by Word-Level Tag Sequence Modeling
In this paper, we transform tag recommendation into a word-based text generation problem and introduce a sequence-to-sequence model.
Neural Chinese Word Segmentation as Sequence to Sequence Translation
In this paper, we cast the CWS as a sequence translation problem and propose a novel sequence-to-sequence CWS model with an attention-based encoder-decoder framework.
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.
Improving Neural Machine Translation by Achieving Knowledge Transfer with Sentence Alignment Learning
To alleviate this problem, we propose an NMT approach that heightens the adequacy in machine translation by transferring the semantic knowledge learned from bilingual sentence alignment.
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.
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.
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.
Open Domain Event Extraction Using Neural Latent Variable Models
We consider open domain event extraction, the task of extracting unconstraint types of events from news clusters.
Earlier Attention? Aspect-Aware LSTM for Aspect-Based Sentiment Analysis
This paper proposes a novel variant of LSTM, termed as aspect-aware LSTM (AA-LSTM), which incorporates aspect information into LSTM cells in the context modeling stage before the attention mechanism.
Distant Supervision for Relation Extraction with Linear Attenuation Simulation and Non-IID Relevance Embedding
Distant supervision for relation extraction is an efficient method to reduce labor costs and has been widely used to seek novel relational facts in large corpora, which can be identified as a multi-instance multi-label problem.
Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction
Relation Extraction suffers from dramatical performance decrease when training a model on one genre and directly applying it to a new genre, due to the distinct feature distributions.
Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation
Event extraction is of practical utility in natural language processing.
Task-oriented Word Embedding for Text Classification
In this paper, we propose a task-oriented word embedding method and apply it to the text classification task.
Ranked #21 on
Text Classification
on AG News
Zewen at SemEval-2018 Task 1: An Ensemble Model for Affect Prediction in Tweets
This paper presents a method for Affect in Tweets, which is the task to automatically determine the intensity of emotions and intensity of sentiment of tweets.
BIT at SemEval-2017 Task 1: Using Semantic Information Space to Evaluate Semantic Textual Similarity
This paper presents three systems for semantic textual similarity (STS) evaluation at SemEval-2017 STS task.
QLUT at SemEval-2017 Task 2: Word Similarity Based on Word Embedding and Knowledge Base
The techniques of our runs mainly make use of the word embeddings and the knowledge-based method.
Conceptualization Topic Modeling
Recently, topic modeling has been widely used to discover the abstract topics in text corpora.
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.
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.
Guided Filter based Edge-preserving Image Non-blind Deconvolution
In the denoising step, the guided filter is used with the two obtained images for efficient edge-preserving filtering.
