31 citations · 211 across the 22 of their papers we have counts for
23 papers
Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Experts
Wenhao Yu, Chenguang Zhu, Lianhui Qin +3
Generative commonsense reasoning (GCR) in natural language is to reason about the commonsense while generating coherent text. Recent years have seen a surge of interest in improvin…
An Empirical Study of Training End-to-End Vision-and-Language Transformers
Zi-Yi Dou, Yichong Xu, Zhe Gan +9
Vision-and-language (VL) pre-training has proven to be highly effective on various VL downstream tasks. While recent work has shown that fully transformer-based VL models can be mo…
SYNERGY: Building Task Bots at Scale Using Symbolic Knowledge and Machine Teaching
Baolin Peng, Chunyuan Li, Zhu Zhang +3
In this paper we explore the use of symbolic knowledge and machine teaching to reduce human data labeling efforts in building neural task bots. We propose SYNERGY, a hybrid learnin…
Leveraging Knowledge in Multilingual Commonsense Reasoning
Yuwei Fang, Shuohang Wang, Yichong Xu +4
Commonsense reasoning (CSR) requires the model to be equipped with general world knowledge. While CSR is a language-agnostic process, most comprehensive knowledge sources are in fe…
End-to-End Segmentation-based News Summarization
Yang Liu, Chenguang Zhu, Michael Zeng
In this paper, we bring a new way of digesting news content by introducing the task of segmenting a news article into multiple sections and generating the corresponding summary to…
Summ^N: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents
Yusen Zhang, Ansong Ni, Ziming Mao +6
Text summarization helps readers capture salient information from documents, news, interviews, and meetings. However, most state-of-the-art pretrained language models (LM) are unab…
DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization
Ziming Mao, Chen Henry Wu, Ansong Ni +7
Transformer-based models have achieved state-of-the-art performance on short-input summarization. However, they still struggle with summarizing longer text. In this paper, we prese…
Dict-BERT: Enhancing Language Model Pre-training with Dictionary
Wenhao Yu, Chenguang Zhu, Yuwei Fang +5
Pre-trained language models (PLMs) aim to learn universal language representations by conducting self-supervised training tasks on large-scale corpora. Since PLMs capture word sema…
KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering
Donghan Yu, Chenguang Zhu, Yuwei Fang +6
Current Open-Domain Question Answering (ODQA) model paradigm often contains a retrieving module and a reading module. Given an input question, the reading module predicts the answe…
An Exploratory Study on Long Dialogue Summarization: What Works and What's Next
Yusen Zhang, Ansong Ni, Tao Yu +6
Dialogue summarization helps readers capture salient information from long conversations in meetings, interviews, and TV series. However, real-world dialogues pose a great challeng…
DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization
Ming Zhong, Yang Liu, Yichong Xu +2
Dialogue is an essential part of human communication and cooperation. Existing research mainly focuses on short dialogue scenarios in a one-on-one fashion. However, multi-person in…
Does Knowledge Help General NLU? An Empirical Study
Ruochen Xu, Yuwei Fang, Chenguang Zhu +1
It is often observed in knowledge-centric tasks (e.g., common sense question and answering, relation classification) that the integration of external knowledge such as entity repre…
Want To Reduce Labeling Cost? GPT-3 Can Help
Shuohang Wang, Yang Liu, Yichong Xu +2
Data annotation is a time-consuming and labor-intensive process for many NLP tasks. Although there exist various methods to produce pseudo data labels, they are often task-specific…
Retrieval Enhanced Model for Commonsense Generation
Han Wang, Yang Liu, Chenguang Zhu +4
Commonsense generation is a challenging task of generating a plausible sentence describing an everyday scenario using provided concepts. Its requirement of reasoning over commonsen…
Sentence-Permuted Paragraph Generation
Wenhao Yu, Chenguang Zhu, Tong Zhao +2
Generating paragraphs of diverse contents is important in many applications. Existing generation models produce similar contents from homogenized contexts due to the fixed left-to-…
Fusing Context Into Knowledge Graph for Commonsense Question Answering
Yichong Xu, Chenguang Zhu, Ruochen Xu +3
Commonsense question answering (QA) requires a model to grasp commonsense and factual knowledge to answer questions about world events. Many prior methods couple language modeling…
JAKET: Joint Pre-training of Knowledge Graph and Language Understanding
Donghan Yu, Chenguang Zhu, Yiming Yang +1
Knowledge graphs (KGs) contain rich information about world knowledge, entities and relations. Thus, they can be great supplements to existing pre-trained language models. However,…
SPLAT: Speech-Language Joint Pre-Training for Spoken Language Understanding
Yu-An Chung, Chenguang Zhu, Michael Zeng
Spoken language understanding (SLU) requires a model to analyze input acoustic signal to understand its linguistic content and make predictions. To boost the models' performance, v…
Accelerating Real-Time Question Answering via Question Generation
Yuwei Fang, Shuohang Wang, Zhe Gan +3
Although deep neural networks have achieved tremendous success for question answering (QA), they are still suffering from heavy computational and energy cost for real product deplo…
Meta Dialogue Policy Learning
Yumo Xu, Chenguang Zhu, Baolin Peng +1
Dialog policy determines the next-step actions for agents and hence is central to a dialogue system. However, when migrated to novel domains with little data, a policy model can fa…
Filtered Inner Product Projection for Crosslingual Embedding Alignment
Vin Sachidananda, Ziyi Yang, Chenguang Zhu
Due to widespread interest in machine translation and transfer learning, there are numerous algorithms for mapping multiple embeddings to a shared representation space. Recently, t…
Boosting Naturalness of Language in Task-oriented Dialogues via Adversarial Training
Chenguang Zhu
The natural language generation (NLG) module in a task-oriented dialogue system produces user-facing utterances conveying required information. Thus, it is critical for the generat…
Few-shot Natural Language Generation for Task-Oriented Dialog
Baolin Peng, Chenguang Zhu, Chunyuan Li +4
As a crucial component in task-oriented dialog systems, the Natural Language Generation (NLG) module converts a dialog act represented in a semantic form into a response in natural…