568 citations · 6.2k across the 157 of their papers we have counts for
14 papers · 1 filter
PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation
Juncheng Wan, Jian Yang, Shuming Ma +4
While end-to-end neural machine translation (NMT) has achieved impressive progress, noisy input usually leads models to become fragile and unstable. Generating adversarial examples…
Learning Logic Rules for Document-level Relation Extraction
Dongyu Ru, Changzhi Sun, Jiangtao Feng +5
Document-level relation extraction aims to identify relations between entities in a whole document. Prior efforts to capture long-range dependencies have relied heavily on implicit…
Reciprocal Supervised Learning Improves Neural Machine Translation
Minkai Xu, Mingxuan Wang, Zhouhan Lin +3
Despite the recent success on image classification, self-training has only achieved limited gains on structured prediction tasks such as neural machine translation (NMT). This is m…
Glancing Transformer for Non-Autoregressive Neural Machine Translation
Lihua Qian, Hao Zhou, Yu Bao +5
Recent work on non-autoregressive neural machine translation (NAT) aims at improving the efficiency by parallel decoding without sacrificing the quality. However, existing NAT meth…
CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training
Qipeng Guo, Zhijing Jin, Xipeng Qiu +3
Two important tasks at the intersection of knowledge graphs and natural language processing are graph-to-text (G2T) and text-to-graph (T2G) conversion. Due to the difficulty and hi…
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space
Dongyu Ru, Jiangtao Feng, Lin Qiu +5
Active learning for sentence understanding aims at discovering informative unlabeled data for annotation and therefore reducing the demand for labeled data. We argue that the typic…
Towards Making the Most of BERT in Neural Machine Translation
Jiacheng Yang, Mingxuan Wang, Hao Zhou +4
GPT-2 and BERT demonstrate the effectiveness of using pre-trained language models (LMs) on various natural language processing tasks. However, LM fine-tuning often suffers from cat…
Dynamically Fused Graph Network for Multi-hop Reasoning
Yunxuan Xiao, Yanru Qu, Lin Qiu +4
Text-based question answering (TBQA) has been studied extensively in recent years. Most existing approaches focus on finding the answer to a question within a single paragraph. How…
Triple-to-Text: Converting RDF Triples into High-Quality Natural Languages via Optimizing an Inverse KL Divergence
Yaoming Zhu, Juncheng Wan, Zhiming Zhou +5
Knowledge base is one of the main forms to represent information in a structured way. A knowledge base typically consists of Resource Description Frameworks (RDF) triples which des…
Label-aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition
Zhenghui Wang, Yanru Qu, Liheng Chen +7
We study the problem of named entity recognition (NER) from electronic medical records, which is one of the most fundamental and critical problems for medical text mining. Medical…
Neural Text Generation: Past, Present and Beyond
Sidi Lu, Yaoming Zhu, Weinan Zhang +2
This paper presents a systematic survey on recent development of neural text generation models. Specifically, we start from recurrent neural network language models with the tradit…
Texygen: A Benchmarking Platform for Text Generation Models
Yaoming Zhu, Sidi Lu, Lei Zheng +4
We introduce Texygen, a benchmarking platform to support research on open-domain text generation models. Texygen has not only implemented a majority of text generation models, but…
Long Text Generation via Adversarial Training with Leaked Information
Jiaxian Guo, Sidi Lu, Han Cai +3
Automatically generating coherent and semantically meaningful text has many applications in machine translation, dialogue systems, image captioning, etc. Recently, by combining wit…
Learning text representation using recurrent convolutional neural network with highway layers
Ying Wen, Weinan Zhang, Rui Luo +1
Recently, the rapid development of word embedding and neural networks has brought new inspiration to various NLP and IR tasks. In this paper, we describe a staged hybrid model comb…