Search Results for author: Chenliang Li

Found 35 papers, 14 papers with code

PALM: Pre-training an Autoencoding\&Autoregressive Language Model for Context-conditioned Generation

no code implementations EMNLP 2020 Bin Bi, Chenliang Li, Chen Wu, Ming Yan, Wei Wang, Songfang Huang, Fei Huang, Luo Si

An extensive set of experiments show that PALM achieves new state-of-the-art results on a variety of language generation benchmarks covering generative question answering (Rank 1 on the official MARCO leaderboard), abstractive summarization on CNN/DailyMail as well as Gigaword, question generation on SQuAD, and conversational response generation on Cornell Movie Dialogues.

Abstractive Text Summarization Conversational Response Generation +6

Grid-VLP: Revisiting Grid Features for Vision-Language Pre-training

no code implementations21 Aug 2021 Ming Yan, Haiyang Xu, Chenliang Li, Bin Bi, Junfeng Tian, Min Gui, Wei Wang

Existing approaches to vision-language pre-training (VLP) heavily rely on an object detector based on bounding boxes (regions), where salient objects are first detected from images and then a Transformer-based model is used for cross-modal fusion.

Object Detection

Addressing Semantic Drift in Generative Question Answering with Auxiliary Extraction

no code implementations ACL 2021 Chenliang Li, Bin Bi, Ming Yan, Wei Wang, Songfang Huang

This work focuses on generative QA which aims to generate an abstractive answer to a given question instead of extracting an answer span from a provided passage.

Generative Question Answering Machine Reading Comprehension

Path-based Deep Network for Candidate Item Matching in Recommenders

no code implementations18 May 2021 Houyi Li, Zhihong Chen, Chenliang Li, Rong Xiao, Hongbo Deng, Peng Zhang, Yongchao Liu, Haihong Tang

PDN utilizes Trigger Net to capture the user's interest in each of his/her interacted item, and Similarity Net to evaluate the similarity between each interacted item and the target item based on these items' profile and CF information.

Recommendation Systems

SemVLP: Vision-Language Pre-training by Aligning Semantics at Multiple Levels

no code implementations14 Mar 2021 Chenliang Li, Ming Yan, Haiyang Xu, Fuli Luo, Wei Wang, Bin Bi, Songfang Huang

Vision-language pre-training (VLP) on large-scale image-text pairs has recently witnessed rapid progress for learning cross-modal representations.

A Critical Study on Data Leakage in Recommender System Offline Evaluation

no code implementations21 Oct 2020 Yitong Ji, Aixin Sun, Jie Zhang, Chenliang Li

We show that accessing to different amount of future data may improve or deteriorate a model's recommendation accuracy.

Recommendation Systems

A Re-visit of the Popularity Baseline in Recommender Systems

1 code implementation28 May 2020 Yitong Ji, Aixin Sun, Jie Zhang, Chenliang Li

On the widely used MovieLens dataset, we show that the performance of popularity could be significantly improved by 70% or more, if we consider the popular items at the time point when a user interacts with the system.

Recommendation Systems

CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network

1 code implementation21 May 2020 Cheng Zhao, Chenliang Li, Rong Xiao, Hongbo Deng, Aixin Sun

Given two relevant domains (e. g., Book and Movie), users may have interactions with items in one domain but not in the other domain.

Recommendation Systems

ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance

1 code implementation21 May 2020 Zhihong Chen, Rong Xiao, Chenliang Li, Gangfeng Ye, Haochuan Sun, Hongbo Deng

Most of ranking models are trained only with displayed items (most are hot items), but they are utilized to retrieve items in the entire space which consists of both displayed and non-displayed items (most are long-tail items).

Domain Adaptation Selection bias

PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation

2 code implementations14 Apr 2020 Bin Bi, Chenliang Li, Chen Wu, Ming Yan, Wei Wang, Songfang Huang, Fei Huang, Luo Si

An extensive set of experiments show that PALM achieves new state-of-the-art results on a variety of language generation benchmarks covering generative question answering (Rank 1 on the official MARCO leaderboard), abstractive summarization on CNN/DailyMail as well as Gigaword, question generation on SQuAD, and conversational response generation on Cornell Movie Dialogues.

Abstractive Text Summarization Conversational Response Generation +6

CASE: Context-Aware Semantic Expansion

no code implementations31 Dec 2019 Jialong Han, Aixin Sun, Haisong Zhang, Chenliang Li, Shuming Shi

In this study, we demonstrate that annotations for this task can be harvested at scale from existing corpora, in a fully automatic manner.

Word Sense Disambiguation

Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-Encoders

1 code implementation ACL 2020 Yu Duan, Canwen Xu, Jiaxin Pei, Jialong Han, Chenliang Li

Conditional Text Generation has drawn much attention as a topic of Natural Language Generation (NLG) which provides the possibility for humans to control the properties of generated contents.

Conditional Text Generation

Incorporating External Knowledge into Machine Reading for Generative Question Answering

no code implementations IJCNLP 2019 Bin Bi, Chen Wu, Ming Yan, Wei Wang, Jiangnan Xia, Chenliang Li

Different from existing work on knowledge-aware QA, we focus on a more challenging task of leveraging external knowledge to generate answers in natural language for a given question with context.

Generative Question Answering Reading Comprehension

Exploiting Multiple Embeddings for Chinese Named Entity Recognition

1 code implementation28 Aug 2019 Canwen Xu, Feiyang Wang, Jialong Han, Chenliang Li

Identifying the named entities mentioned in text would enrich many semantic applications at the downstream level.

Chinese Named Entity Recognition NER

Obj-GloVe: Scene-Based Contextual Object Embedding

no code implementations2 Jul 2019 Canwen Xu, Zhenzhong Chen, Chenliang Li

Recently, with the prevalence of large-scale image dataset, the co-occurrence information among classes becomes rich, calling for a new way to exploit it to facilitate inference.

Dimensionality Reduction Image Generation +1

A Capsule Network for Recommendation and Explaining What You Like and Dislike

1 code implementation1 Jul 2019 Chenliang Li, Cong Quan, Li Peng, Yunwei Qi, Yuming Deng, Libing Wu

A sentiment capsule architecture with a novel Routing by Bi-Agreement mechanism is proposed to identify the informative logic unit and the sentiment based representations in user-item level for rating prediction.

A Review-Driven Neural Model for Sequential Recommendation

no code implementations1 Jul 2019 Chenliang Li, Xichuan Niu, Xiangyang Luo, Zhenzhong Chen, Cong Quan

Given a sequence of historical purchased items for a user, we devise a novel hierarchical attention over attention mechanism to capture sequential patterns at both union-level and individual-level.

Targeted Sentiment Analysis: A Data-Driven Categorization

no code implementations9 May 2019 Jiaxin Pei, Aixin Sun, Chenliang Li

Targeted sentiment analysis (TSA), also known as aspect based sentiment analysis (ABSA), aims at detecting fine-grained sentiment polarity towards targets in a given opinion document.

Aspect-Based Sentiment Analysis

Review-Driven Answer Generation for Product-Related Questions in E-Commerce

1 code implementation27 Apr 2019 Shiqian Chen, Chenliang Li, Feng Ji, Wei Zhou, Haiqing Chen

Then, we devise a mechanism to identify the relevant information from the noise-prone review snippets and incorporate this information to guide the answer generation.

Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey

1 code implementation21 Jan 2019 Wei Emma Zhang, Quan Z. Sheng, Ahoud Alhazmi, Chenliang Li

In this article, we review research works that address this difference and generatetextual adversarial examples on DNNs.

DLocRL: A Deep Learning Pipeline for Fine-Grained Location Recognition and Linking in Tweets

no code implementations21 Jan 2019 Canwen Xu, Jing Li, Xiangyang Luo, Jiaxin Pei, Chenliang Li, Donghong Ji

Recognizing and linking such fine-grained location mentions to well-defined location profiles are beneficial for retrieval and recommendation systems.

Recommendation Systems Representation Learning +1

Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding

1 code implementation4 Nov 2018 Peifeng Wang, Jialong Han, Chenliang Li, Rong pan

Recent efforts on this issue suggest training a neighborhood aggregator in conjunction with the conventional entity and relation embeddings, which may help embed new entities inductively via their existing neighbors.

Knowledge Graph Completion Knowledge Graph Embedding

S2SPMN: A Simple and Effective Framework for Response Generation with Relevant Information

no code implementations EMNLP 2018 Jiaxin Pei, Chenliang Li

In this paper, we propose Sequence to Sequence with Prototype Memory Network (S2SPMN) to exploit the relevant information provided by the large dialogue corpus to enhance response generation.

Machine Translation

A Deep Relevance Model for Zero-Shot Document Filtering

1 code implementation ACL 2018 Chenliang Li, Wei Zhou, Feng Ji, Yu Duan, Haiqing Chen

In the era of big data, focused analysis for diverse topics with a short response time becomes an urgent demand.

Sentiment Analysis Text Classification +1

Guiding Generation for Abstractive Text Summarization Based on Key Information Guide Network

no code implementations NAACL 2018 Chenliang Li, Weiran Xu, Si Li, Sheng Gao

Then, we introduce a Key Information Guide Network (KIGN), which encodes the keywords to the key information representation, to guide the process of generation.

Abstractive Text Summarization

Pair-Linking for Collective Entity Disambiguation: Two Could Be Better Than All

no code implementations4 Feb 2018 Minh C. Phan, Aixin Sun, Yi Tay, Jialong Han, Chenliang Li

For the first time, we show that the semantic relationships between the mentioned entities are in fact less dense than expected.

Decision Making Entity Disambiguation

Multi-label Dataless Text Classification with Topic Modeling

1 code implementation5 Nov 2017 Daochen Zha, Chenliang Li

With a few seed words relevant to each category, SMTM conducts multi-label classification for a collection of documents without any labeled document.

Classification General Classification +2

NEXT: A Neural Network Framework for Next POI Recommendation

no code implementations15 Apr 2017 Zhiqian Zhang, Chenliang Li, Zhiyong Wu, Aixin Sun, Dengpan Ye, Xiangyang Luo

Inspired by the recent success of neural networks in many areas, in this paper, we present a simple but effective neural network framework for next POI recommendation, named NEXT.

Representation Learning

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