57 citations · 127 across the 13 of their papers we have counts for
13 papers
What should I Ask: A Knowledge-driven Approach for Follow-up Questions Generation in Conversational Surveys
Yubin Ge, Ziang Xiao, Jana Diesner +3
Generating follow-up questions on the fly could significantly improve conversational survey quality and user experiences by enabling a more dynamic and personalized survey structur…
A Label Dependence-aware Sequence Generation Model for Multi-level Implicit Discourse Relation Recognition
Changxing Wu, Liuwen Cao, Yubin Ge +3
Implicit discourse relation recognition (IDRR) is a challenging but crucial task in discourse analysis. Most existing methods train multiple models to predict multi-level labels in…
Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings
Shaopeng Lai, Ante Wang, Fandong Meng +6
Dominant sentence ordering models can be classified into pairwise ordering models and set-to-sequence models. However, there is little attempt to combine these two types of models,…
Recursively Conditional Gaussian for Ordinal Unsupervised Domain Adaptation
Xiaofeng Liu, Site Li, Yubin Ge +3
The unsupervised domain adaptation (UDA) has been widely adopted to alleviate the data scalability issue, while the existing works usually focus on classifying independently discre…
Improving Tree-Structured Decoder Training for Code Generation via Mutual Learning
Binbin Xie, Jinsong Su, Yubin Ge +4
Code generation aims to automatically generate a piece of code given an input natural language utterance. Currently, among dominant models, it is treated as a sequence-to-tree task…
Embedding Semantic Hierarchy in Discrete Optimal Transport for Risk Minimization
Yubin Ge, Site Li, Xuyang Li +4
The widely-used cross-entropy (CE) loss-based deep networks achieved significant progress w.r.t. the classification accuracy. However, the CE loss can essentially ignore the risk o…
Enhanced Aspect-Based Sentiment Analysis Models with Progressive Self-supervised Attention Learning
Jinsong Su, Jialong Tang, Hui Jiang +6
In aspect-based sentiment analysis (ABSA), many neural models are equipped with an attention mechanism to quantify the contribution of each context word to sentiment prediction. Ho…
Structural Information Preserving for Graph-to-Text Generation
Linfeng Song, Ante Wang, Jinsong Su +4
The task of graph-to-text generation aims at producing sentences that preserve the meaning of input graphs. As a crucial defect, the current state-of-the-art models may mess up or…
Dynamic Context-guided Capsule Network for Multimodal Machine Translation
Huan Lin, Fandong Meng, Jinsong Su +5
Multimodal machine translation (MMT), which mainly focuses on enhancing text-only translation with visual features, has attracted considerable attention from both computer vision a…
Iterative Dual Domain Adaptation for Neural Machine Translation
Jiali Zeng, Yang Liu, Jinsong Su +4
Previous studies on the domain adaptation for neural machine translation (NMT) mainly focus on the one-pass transferring out-of-domain translation knowledge to in-domain NMT model.…
Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models
Tong Che, Xiaofeng Liu, Site Li +4
AI Safety is a major concern in many deep learning applications such as autonomous driving. Given a trained deep learning model, an important natural problem is how to reliably ver…
Neural Collective Entity Linking Based on Recurrent Random Walk Network Learning
Mengge Xue, Weiming Cai, Jinsong Su +4
Benefiting from the excellent ability of neural networks on learning semantic representations, existing studies for entity linking (EL) have resorted to neural networks to exploit…
Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis
Jialong Tang, Ziyao Lu, Jinsong Su +4
In aspect-level sentiment classification (ASC), it is prevalent to equip dominant neural models with attention mechanisms, for the sake of acquiring the importance of each context…