101 citations · 573 across the 65 of their papers we have counts for
27 papers · 1 filter
End-to-End Chinese Parsing Exploiting Lexicons
Yuan Zhang, Zhiyang Teng, Yue Zhang
Chinese parsing has traditionally been solved by three pipeline systems including word-segmentation, part-of-speech tagging and dependency parsing modules. In this paper, we propos…
SemGloVe: Semantic Co-occurrences for GloVe from BERT
Leilei Gan, Zhiyang Teng, Yue Zhang +3
GloVe learns word embeddings by leveraging statistical information from word co-occurrence matrices. However, word pairs in the matrices are extracted from a predefined local conte…
Natural Language Inference in Context -- Investigating Contextual Reasoning over Long Texts
Hanmeng Liu, Leyang Cui, Jian Liu +1
Natural language inference (NLI) is a fundamental NLP task, investigating the entailment relationship between two texts. Popular NLI datasets present the task at sentence-level. Wh…
Layer-Wise Multi-View Learning for Neural Machine Translation
Qiang Wang, Changliang Li, Yue Zhang +2
Traditional neural machine translation is limited to the topmost encoder layer's context representation and cannot directly perceive the lower encoder layers. Existing solutions us…
Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learning
Yuncheng Hua, Yuan-Fang Li, Gholamreza Haffari +2
A compelling approach to complex question answering is to convert the question to a sequence of actions, which can then be executed on the knowledge base to yield the answer, aka t…
Coarse-to-Fine Pre-training for Named Entity Recognition
Mengge Xue, Bowen Yu, Zhenyu Zhang +3
More recently, Named Entity Recognition hasachieved great advances aided by pre-trainingapproaches such as BERT. However, currentpre-training techniques focus on building lan-guage…
Does Chinese BERT Encode Word Structure?
Yile Wang, Leyang Cui, Yue Zhang
Contextualized representations give significantly improved results for a wide range of NLP tasks. Much work has been dedicated to analyzing the features captured by representative…
What Have We Achieved on Text Summarization?
Dandan Huang, Leyang Cui, Sen Yang +4
Deep learning has led to significant improvement in text summarization with various methods investigated and improved ROUGE scores reported over the years. However, gaps still exis…
Online Back-Parsing for AMR-to-Text Generation
Xuefeng Bai, Linfeng Song, Yue Zhang
AMR-to-text generation aims to recover a text containing the same meaning as an input AMR graph. Current research develops increasingly powerful graph encoders to better represent…
Dyadic Speech-based Affect Recognition using DAMI-P2C Parent-child Multimodal Interaction Dataset
Huili Chen, Yue Zhang, Felix Weninger +3
Automatic speech-based affect recognition of individuals in dyadic conversation is a challenging task, in part because of its heavy reliance on manual pre-processing. Traditional a…
Commonsense Knowledge Graph Reasoning by Selection or Generation? Why?
Cunxiang Wang, Jinhang Wu, Luxin Liu +1
Commonsense knowledge graph reasoning(CKGR) is the task of predicting a missing entity given one existing and the relation in a commonsense knowledge graph (CKG). Existing methods…
SemEval-2020 Task 4: Commonsense Validation and Explanation
Cunxiang Wang, Shuailong Liang, Yili Jin +3
In this paper, we present SemEval-2020 Task 4, Commonsense Validation and Explanation (ComVE), which includes three subtasks, aiming to evaluate whether a system can distinguish a…
On Commonsense Cues in BERT for Solving Commonsense Tasks
Leyang Cui, Sijie Cheng, Yu Wu +1
BERT has been used for solving commonsense tasks such as CommonsenseQA. While prior research has found that BERT does contain commonsense information to some extent, there has been…
openXDATA: A Tool for Multi-Target Data Generation and Missing Label Completion
Felix Weninger, Yue Zhang, Rosalind W. Picard
A common problem in machine learning is to deal with datasets with disjoint label spaces and missing labels. In this work, we introduce the openXDATA tool that completes the missin…
Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset
Edwin Zhang, Nikhil Gupta, Raphael Tang +8
We present Covidex, a search engine that exploits the latest neural ranking models to provide information access to the COVID-19 Open Research Dataset curated by the Allen Institut…
Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences
Xiangyu Duan, Baijun Ji, Hao Jia +5
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. Motivated by the abi…
RelEx: A Model-Agnostic Relational Model Explainer
Yue Zhang, David Defazio, Arti Ramesh
In recent years, considerable progress has been made on improving the interpretability of machine learning models. This is essential, as complex deep learning models with millions…
DRTS Parsing with Structure-Aware Encoding and Decoding
Qiankun Fu, Yue Zhang, Jiangming Liu +1
Discourse representation tree structure (DRTS) parsing is a novel semantic parsing task which has been concerned most recently. State-of-the-art performance can be achieved by a ne…
Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach
Wenyu Du, Zhouhan Lin, Yikang Shen +3
It is commonly believed that knowledge of syntactic structure should improve language modeling. However, effectively and computationally efficiently incorporating syntactic structu…
Multiscale Collaborative Deep Models for Neural Machine Translation
Xiangpeng Wei, Heng Yu, Yue Hu +3
Recent evidence reveals that Neural Machine Translation (NMT) models with deeper neural networks can be more effective but are difficult to train. In this paper, we present a Multi…
MuTual: A Dataset for Multi-Turn Dialogue Reasoning
Leyang Cui, Yu Wu, Shujie Liu +2
Non-task oriented dialogue systems have achieved great success in recent years due to largely accessible conversation data and the development of deep learning techniques. Given a…
Multi-Domain Spoken Language Understanding Using Domain- and Task-Aware Parameterization
Libo Qin, Minheng Ni, Yue Zhang +3
Spoken language understanding has been addressed as a supervised learning problem, where a set of training data is available for each domain. However, annotating data for each doma…
Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog
Libo Qin, Xiao Xu, Wanxiang Che +2
Recent studies have shown remarkable success in end-to-end task-oriented dialog system. However, most neural models rely on large training data, which are only available for a cert…
Struct-MMSB: Mixed Membership Stochastic Blockmodels with Interpretable Structured Priors
Yue Zhang, Arti Ramesh
The mixed membership stochastic blockmodel (MMSB) is a popular framework for community detection and network generation. It learns a low-rank mixed membership representation for ea…