290 citations · 1.9k across the 59 of their papers we have counts for
16 papers · 1 filter
Distilling Knowledge Learned in BERT for Text Generation
Yen-Chun Chen, Zhe Gan, Yu Cheng +2
Large-scale pre-trained language model such as BERT has achieved great success in language understanding tasks. However, it remains an open question how to utilize BERT for languag…
Hierarchical Graph Network for Multi-hop Question Answering
Yuwei Fang, Siqi Sun, Zhe Gan +3
In this paper, we present Hierarchical Graph Network (HGN) for multi-hop question answering. To aggregate clues from scattered texts across multiple paragraphs, a hierarchical grap…
DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
Yizhe Zhang, Siqi Sun, Michel Galley +6
We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer). Trained on 147M conversation-like exchanges ext…
Discourse-Aware Neural Extractive Text Summarization
Jiacheng Xu, Zhe Gan, Yu Cheng +1
Recently BERT has been adopted for document encoding in state-of-the-art text summarization models. However, sentence-based extractive models often result in redundant or uninforma…
Meta Module Network for Compositional Visual Reasoning
Wenhu Chen, Zhe Gan, Linjie Li +3
Neural Module Network (NMN) exhibits strong interpretability and compositionality thanks to its handcrafted neural modules with explicit multi-hop reasoning capability. However, mo…
FreeLB: Enhanced Adversarial Training for Natural Language Understanding
Chen Zhu, Yu Cheng, Zhe Gan +3
Adversarial training, which minimizes the maximal risk for label-preserving input perturbations, has proved to be effective for improving the generalization of language models. In…
UNITER: UNiversal Image-TExt Representation Learning
Yen-Chun Chen, Linjie Li, Licheng Yu +5
Joint image-text embedding is the bedrock for most Vision-and-Language (V+L) tasks, where multimodality inputs are simultaneously processed for joint visual and textual understandi…
What Makes A Good Story? Designing Composite Rewards for Visual Storytelling
Junjie Hu, Yu Cheng, Zhe Gan +3
Previous storytelling approaches mostly focused on optimizing traditional metrics such as BLEU, ROUGE and CIDEr. In this paper, we re-examine this problem from a different angle, b…
Contrastively Smoothed Class Alignment for Unsupervised Domain Adaptation
Shuyang Dai, Yu Cheng, Yizhe Zhang +3
Recent unsupervised approaches to domain adaptation primarily focus on minimizing the gap between the source and the target domains through refining the feature generator, in order…
Patient Knowledge Distillation for BERT Model Compression
Siqi Sun, Yu Cheng, Zhe Gan +1
Pre-trained language models such as BERT have proven to be highly effective for natural language processing (NLP) tasks. However, the high demand for computing resources in trainin…
Adversarial Domain Adaptation for Machine Reading Comprehension
Huazheng Wang, Zhe Gan, Xiaodong Liu +3
In this paper, we focus on unsupervised domain adaptation for Machine Reading Comprehension (MRC), where the source domain has a large amount of labeled data, while only unlabeled…
Unsupervised Deep Structured Semantic Models for Commonsense Reasoning
Shuohang Wang, Sheng Zhang, Yelong Shen +4
Commonsense reasoning is fundamental to natural language understanding. While traditional methods rely heavily on human-crafted features and knowledge bases, we explore learning co…
Tactical Rewind: Self-Correction via Backtracking in Vision-and-Language Navigation
Liyiming Ke, Xiujun Li, Yonatan Bisk +6
We present the Frontier Aware Search with backTracking (FAST) Navigator, a general framework for action decoding, that achieves state-of-the-art results on the Room-to-Room (R2R) V…
A Hybrid Retrieval-Generation Neural Conversation Model
Liu Yang, Junjie Hu, Minghui Qiu +6
Intelligent personal assistant systems that are able to have multi-turn conversations with human users are becoming increasingly popular. Most previous research has been focused on…
Relation-Aware Graph Attention Network for Visual Question Answering
Linjie Li, Zhe Gan, Yu Cheng +1
In order to answer semantically-complicated questions about an image, a Visual Question Answering (VQA) model needs to fully understand the visual scene in the image, especially th…
Multi-step Reasoning via Recurrent Dual Attention for Visual Dialog
Zhe Gan, Yu Cheng, Ahmed El Kholy +3
This paper presents a new model for visual dialog, Recurrent Dual Attention Network (ReDAN), using multi-step reasoning to answer a series of questions about an image. In each ques…