Search Results for author: Luo Si

Found 64 papers, 17 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

De-Biased Court's View Generation with Causality

no code implementations EMNLP 2020 Yiquan Wu, Kun Kuang, Yating Zhang, Xiaozhong Liu, Changlong Sun, Jun Xiao, Yueting Zhuang, Luo Si, Fei Wu

Court{'}s view generation is a novel but essential task for legal AI, aiming at improving the interpretability of judgment prediction results and enabling automatic legal document generation.

Text Generation

APE: Argument Pair Extraction from Peer Review and Rebuttal via Multi-task Learning

no code implementations EMNLP 2020 Liying Cheng, Lidong Bing, Qian Yu, Wei Lu, Luo Si

Peer review and rebuttal, with rich interactions and argumentative discussions in between, are naturally a good resource to mine arguments.

Multi-Task Learning Relation Classification

LightNER: A Lightweight Generative Framework with Prompt-guided Attention for Low-resource NER

no code implementations31 Aug 2021 Xiang Chen, Ningyu Zhang, Lei LI, Xin Xie, Shumin Deng, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen

Most existing NER methods rely on extensive labeled data for model training, which struggles in the low-resource scenarios with limited training data.

Few-Shot Learning Language Modelling +2

MELM: Data Augmentation with Masked Entity Language Modeling for Cross-lingual NER

no code implementations31 Aug 2021 Ran Zhou, Ruidan He, Xin Li, Lidong Bing, Erik Cambria, Luo Si, Chunyan Miao

Specifically, when MELM is applied to augment training data of the source language, it achieves up to 3. 5% F1 score improvement for cross-lingual NER.

Cross-Lingual NER Data Augmentation +2

Argument Pair Extraction via Attention-guided Multi-Layer Multi-Cross Encoding

1 code implementation ACL 2021 Liying Cheng, Tianyu Wu, Lidong Bing, Luo Si

Prior research work treats this task as a sequence labeling problem and a binary classification problem on two passages that are directly concatenated together, which has a limitation of not fully utilizing the unique characteristics and inherent relations of two different passages.

MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER

no code implementations ACL 2021 Linlin Liu, Bosheng Ding, Lidong Bing, Shafiq Joty, Luo Si, Chunyan Miao

With the source-language data as well as the translated data, a generation-based multilingual data augmentation method is introduced to further increase diversity by generating synthetic labeled data in multiple languages.

Cross-Lingual NER Data Augmentation +3

Document-level Relation Extraction as Semantic Segmentation

2 code implementations7 Jun 2021 Ningyu Zhang, Xiang Chen, Xin Xie, Shumin Deng, Chuanqi Tan, Mosha Chen, Fei Huang, Luo Si, Huajun Chen

Specifically, we leverage an encoder module to capture the context information of entities and a U-shaped segmentation module over the image-style feature map to capture global interdependency among triples.

Document-level Relation Extraction +1

On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation

no code implementations ACL 2021 Ruidan He, Linlin Liu, Hai Ye, Qingyu Tan, Bosheng Ding, Liying Cheng, Jia-Wei Low, Lidong Bing, Luo Si

It works by adding light-weight adapter modules to a pretrained language model (PrLM) and only updating the parameters of adapter modules when learning on a downstream task.

Language Modelling

A Unified Span-Based Approach for Opinion Mining with Syntactic Constituents

1 code implementation NAACL 2021 Qingrong Xia, Bo Zhang, Rui Wang, Zhenghua Li, Yue Zhang, Fei Huang, Luo Si, Min Zhang

Fine-grained opinion mining (OM) has achieved increasing attraction in the natural language processing (NLP) community, which aims to find the opinion structures of {``}Who expressed what opinions towards what{''} in one sentence.

Multi-Task Learning Opinion Mining

Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialog State Tracking

no code implementations1 Jun 2021 Yinpei Dai, Hangyu Li, Yongbin Li, Jian Sun, Fei Huang, Luo Si, Xiaodan Zhu

Existing dialog state tracking (DST) models are trained with dialog data in a random order, neglecting rich structural information in a dataset.

 Ranked #1 on Multi-domain Dialogue State Tracking on MULTIWOZ 2.1 (using extra training data)

Curriculum Learning Multi-domain Dialogue State Tracking

Leveraging Online Shopping Behaviors as a Proxy for Personal Lifestyle Choices: New Insights into Chronic Disease Prevention Literacy

no code implementations29 Apr 2021 Yongzhen Wang, Xiaozhong Liu, Katy Börner, Jun Lin, Yingnan Ju, Changlong Sun, Luo Si

Objective: Ubiquitous internet access is reshaping the way we live, but it is accompanied by unprecedented challenges in preventing chronic diseases that are usually planted by long exposure to unhealthy lifestyles.

Medical Diagnosis

Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion

1 code implementation27 Apr 2021 Guanglin Niu, Yang Li, Chengguang Tang, Ruiying Geng, Jian Dai, Qiao Liu, Hao Wang, Jian Sun, Fei Huang, Luo Si

Moreover, modeling and inferring complex relations of one-to-many (1-N), many-to-one (N-1), and many-to-many (N-N) by previous knowledge graph completion approaches requires high model complexity and a large amount of training instances.

Few-Shot Learning Knowledge Graph Completion +1

Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings

no code implementations11 Mar 2021 Linlin Liu, Thien Hai Nguyen, Shafiq Joty, Lidong Bing, Luo Si

We operationalize our framework by first proposing a novel sense-aware cross entropy loss to model word senses explicitly.

Cross-Lingual NER NER +3

VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation

1 code implementation ACL 2021 Fuli Luo, Wei Wang, Jiahao Liu, Yijia Liu, Bin Bi, Songfang Huang, Fei Huang, Luo Si

Existing work in multilingual pretraining has demonstrated the potential of cross-lingual transferability by training a unified Transformer encoder for multiple languages.

Language Modelling Question Answering +1

Aspect Sentiment Classification with Document-level Sentiment Preference Modeling

no code implementations ACL 2020 Xiao Chen, Changlong Sun, Jingjing Wang, Shoushan Li, Luo Si, Min Zhang, Guodong Zhou

This justifies the importance of the document-level sentiment preference information to ASC and the effectiveness of our approach capturing such information.

Classification Document-level +3

ENT-DESC: Entity Description Generation by Exploring Knowledge Graph

1 code implementation EMNLP 2020 Liying Cheng, Dekun Wu, Lidong Bing, Yan Zhang, Zhanming Jie, Wei Lu, Luo Si

Previous works on knowledge-to-text generation take as input a few RDF triples or key-value pairs conveying the knowledge of some entities to generate a natural language description.

Graph-to-Sequence Knowledge Graphs +1

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

Tracing the Propagation Path: A Flow Perspective of Representation Learning on Graphs

no code implementations12 Dec 2019 Menghan Wang, Kun Zhang, Gulin Li, Keping Yang, Luo Si

We generalize the propagation strategies of current GCNs as a \emph{"Sink$\to$Source"} mode, which seems to be an underlying cause of the two challenges.

Representation Learning

Rumor Detection on Social Media: Datasets, Methods and Opportunities

no code implementations WS 2019 Quanzhi Li, Qiong Zhang, Luo Si, Yingchi Liu

Social media platforms have been used for information and news gathering, and they are very valuable in many applications.

Review-based Question Generation with Adaptive Instance Transfer and Augmentation

no code implementations ACL 2020 Qian Yu, Lidong Bing, Qiong Zhang, Wai Lam, Luo Si

We propose an iterative learning framework for handling this challenge via adaptive transfer and augmentation of the training instances with the help of the available user-posed question-answer data.

Question Generation

Uncover Sexual Harassment Patterns from Personal Stories by Joint Key Element Extraction and Categorization

no code implementations IJCNLP 2019 Yingchi Liu, Quanzhi Li, Marika Cifor, Xiaozhong Liu, Qiong Zhang, Luo Si

Sexual harassment occurred in a variety of situations, and categorization of the stories and extraction of their key elements will provide great help for the related parties to understand and address sexual harassment.

Syntax-Enhanced Self-Attention-Based Semantic Role Labeling

no code implementations IJCNLP 2019 Yue Zhang, Rui Wang, Luo Si

As a fundamental NLP task, semantic role labeling (SRL) aims to discover the semantic roles for each predicate within one sentence.

Semantic Role Labeling

Symmetric Regularization based BERT for Pair-wise Semantic Reasoning

1 code implementation8 Sep 2019 Weidi Xu, Xingyi Cheng, Kunlong Chen, Wei Wang, Bin Bi, Ming Yan, Chen Wu, Luo Si, Wei Chu, Taifeng Wang

To remedy this, we propose to augment the NSP task to a 3-class categorization task, which includes a category for previous sentence prediction (PSP).

Document-level Machine Reading Comprehension +2

StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding

no code implementations ICLR 2020 Wei Wang, Bin Bi, Ming Yan, Chen Wu, Zuyi Bao, Jiangnan Xia, Liwei Peng, Luo Si

Recently, the pre-trained language model, BERT (and its robustly optimized version RoBERTa), has attracted a lot of attention in natural language understanding (NLU), and achieved state-of-the-art accuracy in various NLU tasks, such as sentiment classification, natural language inference, semantic textual similarity and question answering.

Language Modelling Linguistic Acceptability +5

Syntax-aware Neural Semantic Role Labeling

1 code implementation22 Jul 2019 Qingrong Xia, Zhenghua Li, Min Zhang, Meishan Zhang, Guohong Fu, Rui Wang, Luo Si

Semantic role labeling (SRL), also known as shallow semantic parsing, is an important yet challenging task in NLP.

Semantic Parsing Semantic Role Labeling

Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network

no code implementations ACL 2019 Jingjing Wang, Changlong Sun, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, Guodong Zhou

This paper extends the research to interactive reviews and proposes a new research task, namely Aspect Sentiment Classification towards Question-Answering (ASC-QA), for real-world applications.

Classification General Classification +2

Semi-supervised Domain Adaptation for Dependency Parsing

1 code implementation ACL 2019 Zhenghua Li, Xue Peng, Min Zhang, Rui Wang, Luo Si

During the past decades, due to the lack of sufficient labeled data, most studies on cross-domain parsing focus on unsupervised domain adaptation, assuming there is no target-domain training data.

Chinese Dependency Parsing Dependency Parsing +2

Multi-Instance Learning for End-to-End Knowledge Base Question Answering

no code implementations6 Mar 2019 Mengxi Wei, Yifan He, Qiong Zhang, Luo Si

This paper proposes a novel approach based on multiple instance learning to address the problem of noisy answers by exploring consensus among answers to the same question in training end-to-end KBQA models.

Curriculum Learning Knowledge Base Question Answering +1

A Deep Cascade Model for Multi-Document Reading Comprehension

no code implementations28 Nov 2018 Ming Yan, Jiangnan Xia, Chen Wu, Bin Bi, Zhongzhou Zhao, Ji Zhang, Luo Si, Rui Wang, Wei Wang, Haiqing Chen

To address this problem, we develop a novel deep cascade learning model, which progressively evolves from the document-level and paragraph-level ranking of candidate texts to more precise answer extraction with machine reading comprehension.

Document-level Machine Reading Comprehension +1

Alibaba Submission for WMT18 Quality Estimation Task

no code implementations WS 2018 Jiayi Wang, Kai Fan, Bo Li, Fengming Zhou, Boxing Chen, Yangbin Shi, Luo Si

The goal of WMT 2018 Shared Task on Translation Quality Estimation is to investigate automatic methods for estimating the quality of machine translation results without reference translations.

Automatic Post-Editing Language Modelling

"Bilingual Expert" Can Find Translation Errors

1 code implementation25 Jul 2018 Kai Fan, Jiayi Wang, Bo Li, Fengming Zhou, Boxing Chen, Luo Si

Recent advances in statistical machine translation via the adoption of neural sequence-to-sequence models empower the end-to-end system to achieve state-of-the-art in many WMT benchmarks.

Language Modelling Machine Translation

Supervised Treebank Conversion: Data and Approaches

no code implementations ACL 2018 Xinzhou Jiang, Zhenghua Li, Bo Zhang, Min Zhang, Sheng Li, Luo Si

Treebank conversion is a straightforward and effective way to exploit various heterogeneous treebanks for boosting parsing performance.

Dependency Parsing Multi-Task Learning

NAI-SEA at SemEval-2018 Task 5: An Event Search System

no code implementations SEMEVAL 2018 Yingchi Liu, Quanzhi Li, Luo Si

In this paper, we describe Alibaba{'}s participating system in the semEval-2018 Task5: Counting Events and Participants in the Long Tail.

Document-level

Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks

no code implementations28 May 2018 Yabo Ni, Dan Ou, Shichen Liu, Xiang Li, Wenwu Ou, An-Xiang Zeng, Luo Si

In this work, we propose to learn universal user representations across multiple tasks for more e ective personalization.

Alibaba at IJCNLP-2017 Task 2: A Boosted Deep System for Dimensional Sentiment Analysis of Chinese Phrases

no code implementations IJCNLP 2017 Xin Zhou, Jian Wang, Xu Xie, Changlong Sun, Luo Si

For word level task our best run achieved MAE 0. 545 (ranked 2nd), PCC 0. 892 (ranked 2nd) in valence prediction and MAE 0. 857 (ranked 1st), PCC 0. 678 (ranked 2nd) in arousal prediction.

Feature Engineering Part-Of-Speech Tagging +1

Recommending Complementary Products in E-Commerce Push Notifications with a Mixture Model Approach

no code implementations25 Jul 2017 Huasha Zhao, Luo Si, Xiaogang Li, Qiong Zhang

The item with the highest predicted open rate is then chosen to be included in the push notification message for each user.

Product Recommendation

Cascade Ranking for Operational E-commerce Search

no code implementations7 Jun 2017 Shichen Liu, Fei Xiao, Wenwu Ou, Luo Si

Real-world search applications often involve multiple factors of preferences or constraints with respect to user experience and computational costs such as search accuracy, search latency, size of search results and total CPU cost, while most existing search solutions only address one or two factors; 2).

A Joint Probabilistic Classification Model of Relevant and Irrelevant Sentences in Mathematical Word Problems

no code implementations21 Nov 2014 Suleyman Cetintas, Luo Si, Yan Ping Xin, Dake Zhang, Joo Young Park, Ron Tzur

Identification of relevant and irrelevant sentences in math word problems is an important step for calculating the difficulty levels of such problems.

Classification General Classification +2

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