204 citations · 344 across the 21 of their papers we have counts for
21 papers
P-Transformer: Towards Better Document-to-Document Neural Machine Translation
Yachao Li, Junhui Li, Jing Jiang +3
Directly training a document-to-document (Doc2Doc) neural machine translation (NMT) via Transformer from scratch, especially on small datasets usually fails to converge. Our dedica…
Exploring and Adapting Chinese GPT to Pinyin Input Method
Minghuan Tan, Yong Dai, Duyu Tang +5
While GPT has become the de-facto method for text generation tasks, its application to pinyin input method remains unexplored. In this work, we make the first exploration to levera…
NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset
Qiyuan Zhang, Lei Wang, Sicheng Yu +4
While diverse question answering (QA) datasets have been proposed and contributed significantly to the development of deep learning models for QA tasks, the existing datasets fall…
Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision
Yang Li, Guodong Long, Tao Shen +1
Distant supervision uses triple facts in knowledge graphs to label a corpus for relation extraction, leading to wrong labeling and long-tail problems. Some works use the hierarchy…
Complex Knowledge Base Question Answering: A Survey
Yunshi Lan, Gaole He, Jinhao Jiang +3
Knowledge base question answering (KBQA) aims to answer a question over a knowledge base (KB). Early studies mainly focused on answering simple questions over KBs and achieved grea…
Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translation
Cunxiao Du, Zhaopeng Tu, Jing Jiang
We propose a new training objective named order-agnostic cross entropy (OaXE) for fully non-autoregressive translation (NAT) models. OaXE improves the standard cross-entropy loss t…
A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions
Yunshi Lan, Gaole He, Jinhao Jiang +3
Knowledge base question answering (KBQA) aims to answer a question over a knowledge base (KB). Recently, a large number of studies focus on semantically or syntactically complicate…
Investigating Math Word Problems using Pretrained Multilingual Language Models
Minghuan Tan, Lei Wang, Lingxiao Jiang +1
In this paper, we revisit math word problems~(MWPs) from the cross-lingual and multilingual perspective. We construct our MWP solvers over pretrained multilingual language models u…
Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals
Gaole He, Yunshi Lan, Jing Jiang +2
Multi-hop Knowledge Base Question Answering (KBQA) aims to find the answer entities that are multiple hops away in the Knowledge Base (KB) from the entities in the question. A majo…
A BERT-based Dual Embedding Model for Chinese Idiom Prediction
Minghuan Tan, Jing Jiang
Chinese idioms are special fixed phrases usually derived from ancient stories, whose meanings are oftentimes highly idiomatic and non-compositional. The Chinese idiom prediction ta…
Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention
Yang Li, Tao Shen, Guodong Long +3
Wrong labeling problem and long-tail relations are two main challenges caused by distant supervision in relation extraction. Recent works alleviate the wrong labeling by selective…
Counterfactual Variable Control for Robust and Interpretable Question Answering
Sicheng Yu, Yulei Niu, Shuohang Wang +2
Deep neural network based question answering (QA) models are neither robust nor explainable in many cases. For example, a multiple-choice QA model, tested without any input of ques…
Cross-Thought for Sentence Encoder Pre-training
Shuohang Wang, Yuwei Fang, Siqi Sun +4
In this paper, we propose Cross-Thought, a novel approach to pre-training sequence encoder, which is instrumental in building reusable sequence embeddings for large-scale NLP tasks…
Context Modeling with Evidence Filter for Multiple Choice Question Answering
Sicheng Yu, Hao Zhang, Wei Jing +1
Multiple-Choice Question Answering (MCQA) is a challenging task in machine reading comprehension. The main challenge in MCQA is to extract "evidence" from the given context that su…
Interpretable Rumor Detection in Microblogs by Attending to User Interactions
Ling Min Serena Khoo, Hai Leong Chieu, Zhong Qian +1
We address rumor detection by learning to differentiate between the community's response to real and fake claims in microblogs. Existing state-of-the-art models are based on tree m…
Multi-level Head-wise Match and Aggregation in Transformer for Textual Sequence Matching
Shuohang Wang, Yunshi Lan, Yi Tay +2
Transformer has been successfully applied to many natural language processing tasks. However, for textual sequence matching, simple matching between the representation of a pair of…
Multimodal Story Generation on Plural Images
Jing Jiang
Traditionally, text generation models take in a sequence of text as input, and iteratively generate the next most probable word using pre-trained parameters. In this work, we propo…
Two-Headed Monster And Crossed Co-Attention Networks
Yaoyiran Li, Jing Jiang
This paper presents some preliminary investigations of a new co-attention mechanism in neural transduction models. We propose a paradigm, termed Two-Headed Monster (THM), which con…
What does BERT Learn from Multiple-Choice Reading Comprehension Datasets?
Chenglei Si, Shuohang Wang, Min-Yen Kan +1
Multiple-Choice Reading Comprehension (MCRC) requires the model to read the passage and question, and select the correct answer among the given options. Recent state-of-the-art mod…
U-Net Based Multi-instance Video Object Segmentation
Heguang Liu, Jingle Jiang
Multi-instance video object segmentation is to segment specific instances throughout a video sequence in pixel level, given only an annotated first frame. In this paper, we impleme…
A Co-Matching Model for Multi-choice Reading Comprehension
Shuohang Wang, Mo Yu, Shiyu Chang +1
Multi-choice reading comprehension is a challenging task, which involves the matching between a passage and a question-answer pair. This paper proposes a new co-matching approach t…