Search Results for author: Sujian Li

Found 112 papers, 29 papers with code

阅读分级相关研究综述(A Survey of Leveled Reading)

no code implementations CCL 2021 Simin Rao, Hua Zheng, Sujian Li

“阅读分级的概念在二十世纪早期就被教育工作者提出, 随着人们对阅读变得越来越重视, 阅读分级引起了越来越多的关注, 自动阅读分级技术也得到了一定程度的发展。本文总结了近年来的阅读分级领域的研究进展, 首先介绍了阅读分级现有的标准和随之而产生的各种体系和语料资源。在此基础之上整理了在自动阅读分级工作已经广泛应用的三类方法:公式法、传统的机器学习方法和最近热门的深度学习方法, 并结合实验结果梳理了三类方法存在的弊利, 以及可以改进的方向。最后本文还对阅读分级的未来发展方向以及可以应用的领域进行了总结和展望。”

A Transition-based Method for Complex Question Understanding

no code implementations COLING 2022 Yu Xia, Wenbin Jiang, Yajuan Lyu, Sujian Li

Existing works are based on end-to-end neural models which do not explicitly model the intermediate states and lack interpretability for the parsing process.

Learn and Review: Enhancing Continual Named Entity Recognition via Reviewing Synthetic Samples

no code implementations Findings (ACL) 2022 Yu Xia, Quan Wang, Yajuan Lyu, Yong Zhu, Wenhao Wu, Sujian Li, Dai Dai

However, the existing method depends on the relevance between tasks and is prone to inter-type confusion. In this paper, we propose a novel two-stage framework Learn-and-Review (L&R) for continual NER under the type-incremental setting to alleviate the above issues. Specifically, for the learning stage, we distill the old knowledge from teacher to a student on the current dataset.

Continual Named Entity Recognition named-entity-recognition +2

Semi-Automatic Construction of Text-to-SQL Data for Domain Transfer

1 code implementation ACL (IWPT) 2021 Tianyi Li, Sujian Li, Mark Steedman

Strong and affordable in-domain data is a desirable asset when transferring trained semantic parsers to novel domains.


Cross-Lingual Leveled Reading Based on Language-Invariant Features

no code implementations Findings (EMNLP) 2021 Simin Rao, Hua Zheng, Sujian Li

Specifically, we focus on adversarial training and cross-lingual pre-training method to transfer the LR knowledge learned from annotated data in the resource-rich English language to Chinese.

Research on Discourse Parsing: from the Dependency View

no code implementations AACL (iwdp) 2020 Sujian Li

Discourse parsing aims to comprehensively acquire the logical structure of the whole text which may be helpful to some downstream applications such as summarization, reading comprehension, QA and so on.

Discourse Parsing Reading Comprehension

Refining Data for Text Generation

no code implementations CCL 2020 Wenyu Guan, Qianying Liu, Tianyi Li, Sujian Li

To solve this problem, we propose a two-step approach which first selects and orders the important data records and then generates text from the noise-reduced data.

Data-to-Text Generation Learning-To-Rank

CoUDA: Coherence Evaluation via Unified Data Augmentation

1 code implementation31 Mar 2024 Dawei Zhu, Wenhao Wu, YiFan Song, Fangwei Zhu, Ziqiang Cao, Sujian Li

Due to the scarcity of annotated data, data augmentation is commonly used for training coherence evaluation models.

Coherence Evaluation Data Augmentation

Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

1 code implementation4 Mar 2024 YiFan Song, Da Yin, Xiang Yue, Jie Huang, Sujian Li, Bill Yuchen Lin

This iterative cycle of exploration and training fosters continued improvement in the agents.

Contrastive Learning

Retrieval-based Full-length Wikipedia Generation for Emergent Events

no code implementations28 Feb 2024 Jiebin Zhang, Eugene J. Yu, Qinyu Chen, Chenhao Xiong, Dawei Zhu, Han Qian, Mingbo Song, Xiaoguang Li, Qun Liu, Sujian Li

In today's fast-paced world, the growing demand to quickly generate comprehensive and accurate Wikipedia documents for emerging events is both crucial and challenging.


Selecting Large Language Model to Fine-tune via Rectified Scaling Law

no code implementations4 Feb 2024 Haowei Lin, Baizhou Huang, Haotian Ye, Qinyu Chen, ZiHao Wang, Sujian Li, Jianzhu Ma, Xiaojun Wan, James Zou, Yitao Liang

The ever-growing ecosystem of LLMs has posed a challenge in selecting the most appropriate pre-trained model to fine-tune amidst a sea of options.

Language Modelling Large Language Model

KBioXLM: A Knowledge-anchored Biomedical Multilingual Pretrained Language Model

1 code implementation20 Nov 2023 Lei Geng, Xu Yan, Ziqiang Cao, Juntao Li, Wenjie Li, Sujian Li, Xinjie Zhou, Yang Yang, Jun Zhang

We achieve a biomedical multilingual corpus by incorporating three granularity knowledge alignments (entity, fact, and passage levels) into monolingual corpora.

Relation XLM-R

Rationale-Enhanced Language Models are Better Continual Relation Learners

1 code implementation10 Oct 2023 Weimin Xiong, YiFan Song, Peiyi Wang, Sujian Li

Continual relation extraction (CRE) aims to solve the problem of catastrophic forgetting when learning a sequence of newly emerging relations.

Continual Relation Extraction Relation +1

PoSE: Efficient Context Window Extension of LLMs via Positional Skip-wise Training

1 code implementation19 Sep 2023 Dawei Zhu, Nan Yang, Liang Wang, YiFan Song, Wenhao Wu, Furu Wei, Sujian Li

To decouple train length from target length for efficient context window extension, we propose Positional Skip-wisE (PoSE) training that smartly simulates long inputs using a fixed context window.

2k Position

RestGPT: Connecting Large Language Models with Real-World RESTful APIs

no code implementations11 Jun 2023 YiFan Song, Weimin Xiong, Dawei Zhu, Wenhao Wu, Han Qian, Mingbo Song, Hailiang Huang, Cheng Li, Ke Wang, Rong Yao, Ye Tian, Sujian Li

To address the practical challenges of tackling complex instructions, we propose RestGPT, which exploits the power of LLMs and conducts a coarse-to-fine online planning mechanism to enhance the abilities of task decomposition and API selection.

Contrastive Bootstrapping for Label Refinement

1 code implementation7 Jun 2023 Shudi Hou, Yu Xia, Muhao Chen, Sujian Li

Traditional text classification typically categorizes texts into pre-defined coarse-grained classes, from which the produced models cannot handle the real-world scenario where finer categories emerge periodically for accurate services.

Clustering text-classification +1

RepCL: Exploring Effective Representation for Continual Text Classification

no code implementations12 May 2023 YiFan Song, Peiyi Wang, Dawei Zhu, Tianyu Liu, Zhifang Sui, Sujian Li

Continual learning (CL) aims to constantly learn new knowledge over time while avoiding catastrophic forgetting on old tasks.

Continual Learning Representation Learning +2

Improving Sentence Similarity Estimation for Unsupervised Extractive Summarization

1 code implementation24 Feb 2023 Shichao Sun, Ruifeng Yuan, Wenjie Li, Sujian Li

Unsupervised extractive summarization aims to extract salient sentences from a document as the summary without labeled data.

Contrastive Learning Extractive Summarization +3

WeCheck: Strong Factual Consistency Checker via Weakly Supervised Learning

1 code implementation20 Dec 2022 Wenhao Wu, Wei Li, Xinyan Xiao, Jiachen Liu, Sujian Li, Yajuan Lv

As a result, they perform poorly on the real generated text and are biased heavily by their single-source upstream tasks.

Natural Language Inference Question Answering +2

Consecutive Question Generation via Dynamic Multitask Learning

no code implementations16 Nov 2022 Yunji Li, Sujian Li, Xing Shi

In this paper, we propose the task of consecutive question generation (CQG), which generates a set of logically related question-answer pairs to understand a whole passage, with a comprehensive consideration of the aspects including accuracy, coverage, and informativeness.

Data Augmentation Informativeness +2

FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness

no code implementations1 Nov 2022 Wenhao Wu, Wei Li, Jiachen Liu, Xinyan Xiao, Ziqiang Cao, Sujian Li, Hua Wu

We first measure a model's factual robustness by its success rate to defend against adversarial attacks when generating factual information.

Abstractive Text Summarization

Precisely the Point: Adversarial Augmentations for Faithful and Informative Text Generation

no code implementations22 Oct 2022 Wenhao Wu, Wei Li, Jiachen Liu, Xinyan Xiao, Sujian Li, Yajuan Lyu

Though model robustness has been extensively studied in language understanding, the robustness of Seq2Seq generation remains understudied.

Informativeness Text Generation

IntTower: the Next Generation of Two-Tower Model for Pre-Ranking System

2 code implementations18 Oct 2022 Xiangyang Li, Bo Chen, Huifeng Guo, Jingjie Li, Chenxu Zhu, Xiang Long, Sujian Li, Yichao Wang, Wei Guo, Longxia Mao, JinXing Liu, Zhenhua Dong, Ruiming Tang

FE-Block module performs fine-grained and early feature interactions to capture the interactive signals between user and item towers explicitly and CIR module leverages a contrastive interaction regularization to further enhance the interactions implicitly.

Learning Robust Representations for Continual Relation Extraction via Adversarial Class Augmentation

1 code implementation10 Oct 2022 Peiyi Wang, YiFan Song, Tianyu Liu, Binghuai Lin, Yunbo Cao, Sujian Li, Zhifang Sui

In this paper, through empirical studies we argue that this assumption may not hold, and an important reason for catastrophic forgetting is that the learned representations do not have good robustness against the appearance of analogous relations in the subsequent learning process.

Continual Relation Extraction Relation

ConFiguRe: Exploring Discourse-level Chinese Figures of Speech

1 code implementation COLING 2022 Dawei Zhu, Qiusi Zhan, Zhejian Zhou, YiFan Song, Jiebin Zhang, Sujian Li

Different from previous token-level or sentence-level counterparts, ConFiguRe aims at extracting a figurative unit from discourse-level context, and classifying the figurative unit into the right figure type.

Natural Language Understanding Sentence

Visual Subtitle Feature Enhanced Video Outline Generation

no code implementations24 Aug 2022 Qi Lv, Ziqiang Cao, Wenrui Xie, Derui Wang, Jingwen Wang, Zhiwei Hu, Tangkun Zhang, Ba Yuan, Yuanhang Li, Min Cao, Wenjie Li, Sujian Li, Guohong Fu

Furthermore, based on the similarity between video outlines and textual outlines, we use a large number of articles with chapter headings to pretrain our model.

Headline Generation Navigate +4

Revising Image-Text Retrieval via Multi-Modal Entailment

no code implementations22 Aug 2022 Xu Yan, Chunhui Ai, Ziqiang Cao, Min Cao, Sujian Li, Wenjie Li, Guohong Fu

While the builders of existing image-text retrieval datasets strive to ensure that the caption matches the linked image, they cannot prevent a caption from fitting other images.

Natural Language Inference Retrieval +2

Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting

no code implementations ACL 2021 Yi Cheng, SiYao Li, Bang Liu, Ruihui Zhao, Sujian Li, Chenghua Lin, Yefeng Zheng

This paper explores the task of Difficulty-Controllable Question Generation (DCQG), which aims at generating questions with required difficulty levels.

Question Answering Question Generation +1

BASS: Boosting Abstractive Summarization with Unified Semantic Graph

no code implementations ACL 2021 Wenhao Wu, Wei Li, Xinyan Xiao, Jiachen Liu, Ziqiang Cao, Sujian Li, Hua Wu, Haifeng Wang

Abstractive summarization for long-document or multi-document remains challenging for the Seq2Seq architecture, as Seq2Seq is not good at analyzing long-distance relations in text.

Abstractive Text Summarization Document Summarization +2

Premise-based Multimodal Reasoning: Conditional Inference on Joint Textual and Visual Clues

no code implementations ACL 2022 Qingxiu Dong, Ziwei Qin, Heming Xia, Tian Feng, Shoujie Tong, Haoran Meng, Lin Xu, Weidong Zhan, Sujian Li, Zhongyu Wei, Tianyu Liu, Zuifang Sui

It is a common practice for recent works in vision language cross-modal reasoning to adopt a binary or multi-choice classification formulation taking as input a set of source image(s) and textual query.

Multimodal Reasoning Natural Language Inference +1

A Comprehensive Attempt to Research Statement Generation

no code implementations25 Apr 2021 Wenhao Wu, Sujian Li

For a researcher, writing a good research statement is crucial but costs a lot of time and effort.


First Target and Opinion then Polarity: Enhancing Target-opinion Correlation for Aspect Sentiment Triplet Extraction

no code implementations17 Feb 2021 Lianzhe Huang, Peiyi Wang, Sujian Li, Tianyu Liu, Xiaodong Zhang, Zhicong Cheng, Dawei Yin, Houfeng Wang

Aspect Sentiment Triplet Extraction (ASTE) aims to extract triplets from a sentence, including target entities, associated sentiment polarities, and opinion spans which rationalize the polarities.

Aspect Sentiment Triplet Extraction Sentence

Exploring Text-transformers in AAAI 2021 Shared Task: COVID-19 Fake News Detection in English

1 code implementation7 Jan 2021 Xiangyang Li, Yu Xia, Xiang Long, Zheng Li, Sujian Li

In this paper, we describe our system for the AAAI 2021 shared task of COVID-19 Fake News Detection in English, where we achieved the 3rd position with the weighted F1 score of 0. 9859 on the test set.

Fake News Detection Position

Unifying Discourse Resources with Dependency Framework

1 code implementation CCL 2021 Yi Cheng, Sujian Li, Yueyuan Li

For text-level discourse analysis, there are various discourse schemes but relatively few labeled data, because discourse research is still immature and it is labor-intensive to annotate the inner logic of a text.

Syntax-Aware Graph Attention Network for Aspect-Level Sentiment Classification

no code implementations COLING 2020 Lianzhe Huang, Xin Sun, Sujian Li, Linhao Zhang, Houfeng Wang

In this paper, we exploit syntactic awareness to the model by the graph attention network on the dependency tree structure and external pre-training knowledge by BERT language model, which helps to model the interaction between the context and aspect words better.

Classification Graph Attention +4

LiveQA: A Question Answering Dataset over Sports Live

2 code implementations CCL 2020 Qianying Liu, Sicong Jiang, Yizhong Wang, Sujian Li

In this paper, we introduce LiveQA, a new question answering dataset constructed from play-by-play live broadcast.

Multiple-choice Question Answering

Composing Elementary Discourse Units in Abstractive Summarization

no code implementations ACL 2020 Zhenwen Li, Wenhao Wu, Sujian Li

In this paper, we argue that elementary discourse unit (EDU) is a more appropriate textual unit of content selection than the sentence unit in abstractive summarization.

Abstractive Text Summarization reinforcement-learning +2

Evaluating Text Coherence at Sentence and Paragraph Levels

no code implementations LREC 2020 Sennan Liu, Shuang Zeng, Sujian Li

In this paper, to evaluate text coherence, we propose the paragraph ordering task as well as conducting sentence ordering.

Sentence Sentence Ordering

Incorporating Textual Evidence in Visual Storytelling

no code implementations WS 2019 Tianyi Li, Sujian Li

Previous work on visual storytelling mainly focused on exploring image sequence as evidence for storytelling and neglected textual evidence for guiding story generation.

Object Recognition Visual Storytelling

Tree-structured Decoding for Solving Math Word Problems

no code implementations IJCNLP 2019 Qianying Liu, Wenyv Guan, Sujian Li, Daisuke Kawahara

To address this problem, we propose a tree-structured decoding method that generates the abstract syntax tree of the equation in a top-down manner.


Text Level Graph Neural Network for Text Classification

2 code implementations IJCNLP 2019 Lianzhe Huang, Dehong Ma, Sujian Li, Xiaodong Zhang, Houfeng Wang

Recently, researches have explored the graph neural network (GNN) techniques on text classification, since GNN does well in handling complex structures and preserving global information.

General Classification text-classification +1

Do NLP Models Know Numbers? Probing Numeracy in Embeddings

1 code implementation IJCNLP 2019 Eric Wallace, Yizhong Wang, Sujian Li, Sameer Singh, Matt Gardner

The ability to understand and work with numbers (numeracy) is critical for many complex reasoning tasks.

Question Answering

Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy

1 code implementation10 Sep 2019 Bowen Yu, Zhen-Yu Zhang, Xiaobo Shu, Yubin Wang, Tingwen Liu, Bin Wang, Sujian Li

Joint extraction of entities and relations aims to detect entity pairs along with their relations using a single model.

Relation Extraction

Jointly Modeling Hierarchical and Horizontal Features for Relational Triple Extraction

no code implementations23 Aug 2019 Zhepei Wei, Yantao Jia, Yuan Tian, Mohammad Javad Hosseini, Sujian Li, Mark Steedman, Yi Chang

In this work, we first introduce the hierarchical dependency and horizontal commonality between the two levels, and then propose an entity-enhanced dual tagging framework that enables the triple extraction (TE) task to utilize such interactions with self-learned entity features through an auxiliary entity extraction (EE) task, without breaking the joint decoding of relational triples.

Entity Extraction using GAN graph construction +2

Denoising based Sequence-to-Sequence Pre-training for Text Generation

no code implementations IJCNLP 2019 Liang Wang, Wei Zhao, Ruoyu Jia, Sujian Li, Jingming Liu

This paper presents a new sequence-to-sequence (seq2seq) pre-training method PoDA (Pre-training of Denoising Autoencoders), which learns representations suitable for text generation tasks.

Abstractive Text Summarization Denoising +2

Exploring Sequence-to-Sequence Learning in Aspect Term Extraction

no code implementations ACL 2019 Dehong Ma, Sujian Li, Fangzhao Wu, Xing Xie, Houfeng Wang

Aspect term extraction (ATE) aims at identifying all aspect terms in a sentence and is usually modeled as a sequence labeling problem.

Position Sentence +1

Machine Reading Comprehension: a Literature Review

no code implementations30 Jun 2019 Xin Zhang, An Yang, Sujian Li, Yizhong Wang

Machine reading comprehension aims to teach machines to understand a text like a human and is a new challenging direction in Artificial Intelligence.

Machine Reading Comprehension

An Improved Coarse-to-Fine Method for Solving Generation Tasks

no code implementations ALTA 2019 Wenyv Guan, Qianying Liu, Guangzhi Han, Bin Wang, Sujian Li

The methods first generate a rough sketch in the coarse stage and then use the sketch to get the final result in the fine stage.

Math Math Word Problem Solving +1

Joint Learning for Targeted Sentiment Analysis

no code implementations EMNLP 2018 Dehong Ma, Sujian Li, Houfeng Wang

Targeted sentiment analysis (TSA) aims at extracting targets and classifying their sentiment classes.

Sentiment Analysis Word Embeddings

Chinese Discourse Segmentation Using Bilingual Discourse Commonality

no code implementations30 Aug 2018 Jingfeng Yang, Sujian Li

Discourse segmentation aims to segment Elementary Discourse Units (EDUs) and is a fundamental task in discourse analysis.

Discourse Segmentation Segmentation

Toward Fast and Accurate Neural Discourse Segmentation

1 code implementation EMNLP 2018 Yizhong Wang, Sujian Li, Jingfeng Yang

Discourse segmentation, which segments texts into Elementary Discourse Units, is a fundamental step in discourse analysis.

Discourse Segmentation

Abstractive Summarization Improved by WordNet-based Extractive Sentences

no code implementations4 Aug 2018 Niantao Xie, Sujian Li, Huiling Ren, Qibin Zhai

Experiments on the CNN/Daily Mail dataset show that our models achieve competitive performance with the state-of-the-art ROUGE scores.

Abstractive Text Summarization Sentence

SciDTB: Discourse Dependency TreeBank for Scientific Abstracts

1 code implementation ACL 2018 An Yang, Sujian Li

Annotation corpus for discourse relations benefits NLP tasks such as machine translation and question answering.

Machine Translation Question Answering +1

Adaptations of ROUGE and BLEU to Better Evaluate Machine Reading Comprehension Task

no code implementations WS 2018 An Yang, Kai Liu, Jing Liu, Yajuan Lyu, Sujian Li

Current evaluation metrics to question answering based machine reading comprehension (MRC) systems generally focus on the lexical overlap between the candidate and reference answers, such as ROUGE and BLEU.

Machine Reading Comprehension Question Answering

Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification

no code implementations ACL 2018 Yizhong Wang, Kai Liu, Jing Liu, wei he, Yajuan Lyu, Hua Wu, Sujian Li, Haifeng Wang

Machine reading comprehension (MRC) on real web data usually requires the machine to answer a question by analyzing multiple passages retrieved by search engine.

Machine Reading Comprehension Question Answering

Faithful to the Original: Fact Aware Neural Abstractive Summarization

no code implementations13 Nov 2017 Ziqiang Cao, Furu Wei, Wenjie Li, Sujian Li

While previous abstractive summarization approaches usually focus on the improvement of informativeness, we argue that faithfulness is also a vital prerequisite for a practical abstractive summarization system.

Abstractive Text Summarization Extractive Summarization +3

Deep Stacking Networks for Low-Resource Chinese Word Segmentation with Transfer Learning

no code implementations4 Nov 2017 Jingjing Xu, Xu sun, Sujian Li, Xiaoyan Cai, Bingzhen Wei

In this paper, we propose a deep stacking framework to improve the performance on word segmentation tasks with insufficient data by integrating datasets from diverse domains.

Chinese Word Segmentation Transfer Learning

Interactive Attention Networks for Aspect-Level Sentiment Classification

5 code implementations4 Sep 2017 Dehong Ma, Sujian Li, Xiaodong Zhang, Houfeng Wang

In this paper, we argue that both targets and contexts deserve special treatment and need to be learned their own representations via interactive learning.

Aspect-Based Sentiment Analysis (ABSA) Classification +2

Order-Planning Neural Text Generation From Structured Data

1 code implementation1 Sep 2017 Lei Sha, Lili Mou, Tianyu Liu, Pascal Poupart, Sujian Li, Baobao Chang, Zhifang Sui

Generating texts from structured data (e. g., a table) is important for various natural language processing tasks such as question answering and dialog systems.

Question Answering Table-to-Text Generation

PKU\_ICL at SemEval-2017 Task 10: Keyphrase Extraction with Model Ensemble and External Knowledge

no code implementations SEMEVAL 2017 Liang Wang, Sujian Li

This paper presents a system that participated in SemEval 2017 Task 10 (subtask A and subtask B): Extracting Keyphrases and Relations from Scientific Publications (Augenstein et al., 2017).

Chunking Feature Engineering +5

A Two-Stage Parsing Method for Text-Level Discourse Analysis

1 code implementation ACL 2017 Yizhong Wang, Sujian Li, Houfeng Wang

Previous work introduced transition-based algorithms to form a unified architecture of parsing rhetorical structures (including span, nuclearity and relation), but did not achieve satisfactory performance.

Dependency Parsing Document Summarization +4

Reading and Thinking: Re-read LSTM Unit for Textual Entailment Recognition

no code implementations COLING 2016 Lei Sha, Baobao Chang, Zhifang Sui, Sujian Li

After read the premise again, the model can get a better understanding of the premise, which can also affect the understanding of the hypothesis.

Information Retrieval Machine Translation +4

Towards Time-Aware Knowledge Graph Completion

no code implementations COLING 2016 Tingsong Jiang, Tianyu Liu, Tao Ge, Lei Sha, Baobao Chang, Sujian Li, Zhifang Sui

In this paper, we present a novel time-aware knowledge graph completion model that is able to predict links in a KG using both the existing facts and the temporal information of the facts.

Question Answering Relation Extraction +1

Improving Multi-Document Summarization via Text Classification

no code implementations28 Nov 2016 Ziqiang Cao, Wenjie Li, Sujian Li, Furu Wei

Developed so far, multi-document summarization has reached its bottleneck due to the lack of sufficient training data and diverse categories of documents.

Document Summarization General Classification +3

Joint Copying and Restricted Generation for Paraphrase

no code implementations28 Nov 2016 Ziqiang Cao, Chuwei Luo, Wenjie Li, Sujian Li

In this paper, we develop a novel Seq2Seq model to fuse a copying decoder and a restricted generative decoder.

Abstractive Text Summarization Informativeness +2

Recognizing Implicit Discourse Relations via Repeated Reading: Neural Networks with Multi-Level Attention

no code implementations EMNLP 2016 Yang Liu, Sujian Li

Recognizing implicit discourse relations is a challenging but important task in the field of Natural Language Processing.

Implicit Discourse Relation Classification via Multi-Task Neural Networks

no code implementations9 Mar 2016 Yang Liu, Sujian Li, Xiaodong Zhang, Zhifang Sui

Without discourse connectives, classifying implicit discourse relations is a challenging task and a bottleneck for building a practical discourse parser.

Classification General Classification +3

Joint Learning Templates and Slots for Event Schema Induction

no code implementations NAACL 2016 Lei Sha, Sujian Li, Baobao Chang, Zhifang Sui

Automatic event schema induction (AESI) means to extract meta-event from raw text, in other words, to find out what types (templates) of event may exist in the raw text and what roles (slots) may exist in each event type.

Image Segmentation Semantic Segmentation +1

Component-Enhanced Chinese Character Embeddings

no code implementations EMNLP 2015 Yan-ran Li, Wenjie Li, Fei Sun, Sujian Li

Distributed word representations are very useful for capturing semantic information and have been successfully applied in a variety of NLP tasks, especially on English.

General Classification text-classification +3

Multi-Document Summarization via Discriminative Summary Reranking

no code implementations8 Jul 2015 Xiaojun Wan, Ziqiang Cao, Furu Wei, Sujian Li, Ming Zhou

However, according to our quantitative analysis, none of the existing summarization models can always produce high-quality summaries for different document sets, and even a summarization model with good overall performance may produce low-quality summaries for some document sets.

Document Summarization Multi-Document Summarization +1

A Novel Feature-based Bayesian Model for Query Focused Multi-document Summarization

no code implementations TACL 2013 Jiwei Li, Sujian Li

Both supervised learning methods and LDA based topic model have been successfully applied in the field of query focused multi-document summarization.

Document Summarization Multi-Document Summarization +2

Cannot find the paper you are looking for? You can Submit a new open access paper.