Search Results for author: Sujian Li

Found 81 papers, 14 papers with code

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 +1

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

Premise-based Multimodal Reasoning: A Human-like Cognitive Process

no code implementations15 May 2021 Qingxiu Dong, Ziwei Qin, Heming Xia, Tian Feng, Shoujie Tong, Haoran Meng, Lin Xu, Tianyu Liu, Zuifang Sui, Weidong Zhan, Sujian Li, Zhongyu Wei

Reasoning is one of the major challenges of Human-like AI and has recently attracted intensive attention from natural language processing (NLP) researchers.

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

To address these issues, we propose a novel two-stage method which enhances the correlation between targets and opinions: at stage one, we extract targets and opinions through sequence tagging; then we insert a group of artificial tags named Perceivable Pair, which indicate the span of the target and the opinion, into the sequence to establish correlation for each candidate target-opinion pair.

Aspect Sentiment Triplet Extraction

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

Unifying Discourse Resources with Dependency Framework

1 code implementation1 Jan 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 +2

LiveQA: A Question Answering Dataset over Sports Live

2 code implementations1 Oct 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.

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

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 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 Story Generation +1

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

1 code implementation 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.

Classification General 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

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

1 code implementation 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.

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 Word Problem Solving Semantic Parsing

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

Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning

no code implementations EMNLP 2018 Chen Shi, Qi Chen, Lei Sha, Sujian Li, Xu Sun, Houfeng Wang, Lintao Zhang

The lack of labeled data is one of the main challenges when building a task-oriented dialogue system.

Active Learning

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.

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.

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

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 +2

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

3 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 Classification +1

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 implementation30 Jul 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.

Discourse Parsing

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

no code implementations 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 +1

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 +3

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.

Knowledge Graph Completion Question Answering +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.

Classification Document Summarization +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 Text Generation +1

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.

AttSum: Joint Learning of Focusing and Summarization with Neural Attention

no code implementations COLING 2016 Ziqiang Cao, Wenjie Li, Sujian Li, Furu Wei, Yan-ran Li

Query relevance ranking and sentence saliency ranking are the two main tasks in extractive query-focused summarization.

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 +2

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.

Semantic Segmentation

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 +2

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

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 +1

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