Search Results for author: Chenguang Zhu

Found 48 papers, 23 papers with code

Modeling Entity Knowledge for Fact Verification

no code implementations EMNLP (FEVER) 2021 Yang Liu, Chenguang Zhu, Michael Zeng

Fact verification is a challenging task of identifying the truthfulness of given claims based on the retrieval of relevant evidence texts.

Fact Verification

CLIP-Event: Connecting Text and Images with Event Structures

no code implementations13 Jan 2022 Manling Li, Ruochen Xu, Shuohang Wang, Luowei Zhou, Xudong Lin, Chenguang Zhu, Michael Zeng, Heng Ji, Shih-Fu Chang

Vision-language (V+L) pretraining models have achieved great success in supporting multimedia applications by understanding the alignments between images and text.

Contrastive Learning Event Extraction +1

MLP Architectures for Vision-and-Language Modeling: An Empirical Study

1 code implementation8 Dec 2021 Yixin Nie, Linjie Li, Zhe Gan, Shuohang Wang, Chenguang Zhu, Michael Zeng, Zicheng Liu, Mohit Bansal, Lijuan Wang

Based on this, we ask an even bolder question: can we have an all-MLP architecture for VL modeling, where both VL fusion and the vision encoder are replaced with MLPs?

Language Modelling Visual Question Answering

Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention

no code implementations6 Dec 2021 Yichong Xu, Chenguang Zhu, Shuohang Wang, Siqi Sun, Hao Cheng, Xiaodong Liu, Jianfeng Gao, Pengcheng He, Michael Zeng, Xuedong Huang

In particular, we focus on the task of Commonsense Reasoning, demonstrating that the proposed external attention mechanism can augment existing transformer models and significantly improve the model's reasoning capabilities.

SYNERGY: Building Task Bots at Scale Using Symbolic Knowledge and Machine Teaching

no code implementations21 Oct 2021 Baolin Peng, Chunyuan Li, Zhu Zhang, Jinchao Li, Chenguang Zhu, Jianfeng Gao

We propose SYNERGY, a hybrid learning framework where a task bot is developed in two steps: (i) Symbolic knowledge to neural networks: Large amounts of simulated dialog sessions are generated based on task-specific symbolic knowledge which is represented as a task schema consisting of dialog flows and task-oriented databases.

Leveraging Knowledge in Multilingual Commonsense Reasoning

no code implementations16 Oct 2021 Yuwei Fang, Shuohang Wang, Yichong Xu, Ruochen Xu, Siqi Sun, Chenguang Zhu, Michael Zeng

Then we utilize a diverse of 4 English knowledge sources to provide more comprehensive coverage of knowledge in different formats.

Language Modelling Translation

DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization

1 code implementation15 Oct 2021 Ziming Mao, Chen Henry Wu, Ansong Ni, Yusen Zhang, Rui Zhang, Tao Yu, Budhaditya Deb, Chenguang Zhu, Ahmed H. Awadallah, Dragomir Radev

We introduce consistency loss, which encourages the extractor to approximate the averaged dynamic weights predicted by the generator.

Abstractive Text Summarization

End-to-End Segmentation-based News Summarization

no code implementations15 Oct 2021 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 each section.

Text Generation

Dict-BERT: Enhancing Language Model Pre-training with Dictionary

no code implementations13 Oct 2021 Wenhao Yu, Chenguang Zhu, Yuwei Fang, Donghan Yu, Shuohang Wang, Yichong Xu, Michael Zeng, Meng Jiang

In addition to training with the masked language modeling objective, we propose two novel self-supervised pre-training tasks on word and sentence-level alignment between input text sequence and rare word definitions to enhance language modeling representation with dictionary.

Language Modelling

KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering

no code implementations8 Oct 2021 Donghan Yu, Chenguang Zhu, Yuwei Fang, Wenhao Yu, Shuohang Wang, Yichong Xu, Xiang Ren, Yiming Yang, Michael Zeng

The recent proposed Fusion-in-Decoder (FiD), which is built on top of the pretrained generative model T5, achieves the state-of-the-art performance in the reading module.

Open-Domain Question Answering Passage Retrieval

An Exploratory Study on Long Dialogue Summarization: What Works and What's Next

1 code implementation10 Sep 2021 Yusen Zhang, Ansong Ni, Tao Yu, Rui Zhang, Chenguang Zhu, Budhaditya Deb, Asli Celikyilmaz, Ahmed Hassan Awadallah, Dragomir Radev

Dialogue summarization helps readers capture salient information from long conversations in meetings, interviews, and TV series.

DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization

1 code implementation6 Sep 2021 Ming Zhong, Yang Liu, Yichong Xu, Chenguang Zhu, Michael Zeng

For a dialogue, it corrupts a window of text with dialogue-inspired noise, and guides the model to reconstruct this window based on the content of the remaining conversation.

Denoising Dialogue Understanding +1

Does Knowledge Help General NLU? An Empirical Study

no code implementations1 Sep 2021 Ruochen Xu, Yuwei Fang, Chenguang Zhu, Michael Zeng

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 representation into language models can help provide useful information to boost the performance.

Common Sense Reasoning Language Modelling +2

Retrieval Enhanced Model for Commonsense Generation

1 code implementation Findings (ACL) 2021 Han Wang, Yang Liu, Chenguang Zhu, Linjun Shou, Ming Gong, Yichong Xu, Michael Zeng

Commonsense generation is a challenging task of generating a plausible sentence describing an everyday scenario using provided concepts.

Text Generation

Sentence-Permuted Paragraph Generation

1 code implementation EMNLP 2021 Wenhao Yu, Chenguang Zhu, Tong Zhao, Zhichun Guo, Meng Jiang

Generating paragraphs of diverse contents is important in many applications.

MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization

1 code implementation NAACL 2021 Chenguang Zhu, Yang Liu, Jie Mei, Michael Zeng

MediaSum, a large-scale media interview dataset consisting of 463. 6K transcripts with abstractive summaries.

Transfer Learning

RADDLE: An Evaluation Benchmark and Analysis Platform for Robust Task-oriented Dialog Systems

no code implementations ACL 2021 Baolin Peng, Chunyuan Li, Zhu Zhang, Chenguang Zhu, Jinchao Li, Jianfeng Gao

For task-oriented dialog systems to be maximally useful, it must be able to process conversations in a way that is (1) generalizable with a small number of training examples for new task domains, and (2) robust to user input in various styles, modalities or domains.

Fusing Context Into Knowledge Graph for Commonsense Question Answering

1 code implementation Findings (ACL) 2021 Yichong Xu, Chenguang Zhu, Ruochen Xu, Yang Liu, Michael Zeng, Xuedong Huang

However, although a KG contains rich structural information, it lacks the context to provide a more precise understanding of the concepts.

Knowledge Graphs Language Modelling +2

A Survey of Knowledge-Enhanced Text Generation

3 code implementations9 Oct 2020 Wenhao Yu, Chenguang Zhu, Zaitang Li, Zhiting Hu, Qingyun Wang, Heng Ji, Meng Jiang

To address this issue, researchers have considered incorporating various forms of knowledge beyond the input text into the generation models.

Text Generation

SPLAT: Speech-Language Joint Pre-Training for Spoken Language Understanding

no code implementations NAACL 2021 Yu-An Chung, Chenguang Zhu, Michael Zeng

Besides conducting a self-supervised masked language modeling task on the two individual modules using unpaired speech and text, SPLAT aligns representations from the two modules in a shared latent space using a small amount of paired speech and text.

Language Modelling Spoken Language Understanding

Injecting Entity Types into Entity-Guided Text Generation

2 code implementations EMNLP 2021 Xiangyu Dong, Wenhao Yu, Chenguang Zhu, Meng Jiang

Our model has a multi-step decoder that injects the entity types into the process of entity mention generation.

Text Generation

Accelerating Real-Time Question Answering via Question Generation

no code implementations10 Sep 2020 Yuwei Fang, Shuohang Wang, Zhe Gan, Siqi Sun, Jingjing Liu, Chenguang Zhu

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 deployment.

Data Augmentation Multi-Task Learning +2

Mind The Facts: Knowledge-Boosted Coherent Abstractive Text Summarization

no code implementations27 Jun 2020 Beliz Gunel, Chenguang Zhu, Michael Zeng, Xuedong Huang

In this work, we propose a novel architecture that extends Transformer encoder-decoder architecture in order to improve on these shortcomings.

Abstractive Text Summarization Language Modelling

Filtered Inner Product Projection for Crosslingual Embedding Alignment

no code implementations ICLR 2021 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.

Machine Translation Transfer Learning +1

Meta Dialogue Policy Learning

no code implementations3 Jun 2020 Yumo Xu, Chenguang Zhu, Baolin Peng, Michael Zeng

Dialog policy determines the next-step actions for agents and hence is central to a dialogue system.

Meta-Learning Transfer Learning

Boosting Naturalness of Language in Task-oriented Dialogues via Adversarial Training

no code implementations SIGDIAL (ACL) 2020 Chenguang Zhu

The natural language generation (NLG) module in a task-oriented dialogue system produces user-facing utterances conveying required information.

Text Generation

Few-shot Natural Language Generation for Task-Oriented Dialog

2 code implementations Findings of the Association for Computational Linguistics 2020 Baolin Peng, Chenguang Zhu, Chunyuan Li, Xiujun Li, Jinchao Li, Michael Zeng, Jianfeng Gao

It is pre-trained on a large set of annotated NLG corpus to acquire the controllable generation ability, and fine-tuned with only a few domain-specific labels to adapt to new domains.

Data-to-Text Generation Few-Shot Learning

Leveraging Lead Bias for Zero-shot Abstractive News Summarization

no code implementations25 Dec 2019 Chenguang Zhu, Ziyi Yang, Robert Gmyr, Michael Zeng, Xuedong Huang

A typical journalistic convention in news articles is to deliver the most salient information in the beginning, also known as the lead bias.

Domain Adaptation

SIM: A Slot-Independent Neural Model for Dialogue State Tracking

no code implementations WS 2019 Chenguang Zhu, Michael Zeng, Xuedong Huang

In this paper, we put forward a slot-independent neural model (SIM) to track dialogue states while keeping the model complexity invariant to the number of dialogue slots.

Dialogue State Tracking Task-Oriented Dialogue Systems

Make Lead Bias in Your Favor: A Simple and Effective Method for News Summarization

no code implementations25 Sep 2019 Chenguang Zhu, ZiYi Yang, Robert Gmyr, Michael Zeng, Xuedong Huang

For example, the pretrained model without finetuning outperforms pointer-generator network on CNN/DailyMail dataset.

Embedding Imputation with Grounded Language Information

1 code implementation ACL 2019 Ziyi Yang, Chenguang Zhu, Sachidan, Vin a, Eric Darve

In this paper, we propose an approach for embedding imputation which uses grounded information in the form of a knowledge graph.

Imputation

Out-of-Vocabulary Embedding Imputation with Grounded Language Information by Graph Convolutional Networks

no code implementations ACL 2019 Ziyi Yang, Chenguang Zhu, Vin Sachidananda, Eric Darve

In this paper, we propose an approach for embedding imputation which uses grounded information in the form of a knowledge graph.

Imputation

Parameter-free Sentence Embedding via Orthogonal Basis

1 code implementation IJCNLP 2019 Ziyi Yang, Chenguang Zhu, Weizhu Chen

Inspired by the Gram-Schmidt Process in geometric theory, we build an orthogonal basis of the subspace spanned by a word and its surrounding context in a sentence.

Sentence Embedding Word Embeddings

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