174 citations · 674 across the 31 of their papers we have counts for
31 papers
PoSE: Efficient Context Window Extension of LLMs via Positional Skip-wise Training
Dawei Zhu, Nan Yang, Liang Wang +4
Large Language Models (LLMs) are trained with a pre-defined context length, restricting their use in scenarios requiring long inputs. Previous efforts for adapting LLMs to a longer…
Contrastive Bootstrapping for Label Refinement
Shudi Hou, Yu Xia, Muhao Chen +1
Traditional text classification typically categorizes texts into pre-defined coarse-grained classes, from which the produced models cannot handle the real-world scenario where fine…
Improving Sentence Similarity Estimation for Unsupervised Extractive Summarization
Shichao Sun, Ruifeng Yuan, Wenjie Li +1
Unsupervised extractive summarization aims to extract salient sentences from a document as the summary without labeled data. Recent literatures mostly research how to leverage sent…
ConFiguRe: Exploring Discourse-level Chinese Figures of Speech
Dawei Zhu, Qiusi Zhan, Zhejian Zhou +3
Figures of speech, such as metaphor and irony, are ubiquitous in literature works and colloquial conversations. This poses great challenge for natural language understanding since…
Visual Subtitle Feature Enhanced Video Outline Generation
Qi Lv, Ziqiang Cao, Wenrui Xie +10
With the tremendously increasing number of videos, there is a great demand for techniques that help people quickly navigate to the video segments they are interested in. However, c…
Jointly Modeling Hierarchical and Horizontal Features for Relational Triple Extraction
Zhepei Wei, Yantao Jia, Yuan Tian +4
Recent works on relational triple extraction have shown the superiority of jointly extracting entities and relations over the pipelined extraction manner. However, most existing jo…
Machine Reading Comprehension: a Literature Review
Xin Zhang, An Yang, Sujian Li +1
Machine reading comprehension aims to teach machines to understand a text like a human and is a new challenging direction in Artificial Intelligence. This article summarizes recent…
Chinese Discourse Segmentation Using Bilingual Discourse Commonality
Jingfeng Yang, Sujian Li
Discourse segmentation aims to segment Elementary Discourse Units (EDUs) and is a fundamental task in discourse analysis. For Chinese, previous researches identify EDUs just throug…
Toward Fast and Accurate Neural Discourse Segmentation
Yizhong Wang, Sujian Li, Jingfeng Yang
Discourse segmentation, which segments texts into Elementary Discourse Units, is a fundamental step in discourse analysis. Previous discourse segmenters rely on complicated hand-cr…
Multi-Perspective Context Aggregation for Semi-supervised Cloze-style Reading Comprehension
Liang Wang, Sujian Li, Wei Zhao +4
Cloze-style reading comprehension has been a popular task for measuring the progress of natural language understanding in recent years. In this paper, we design a novel multi-persp…
Abstractive Summarization Improved by WordNet-based Extractive Sentences
Niantao Xie, Sujian Li, Huiling Ren +1
Recently, the seq2seq abstractive summarization models have achieved good results on the CNN/Daily Mail dataset. Still, how to improve abstractive methods with extractive methods i…
SciDTB: Discourse Dependency TreeBank for Scientific Abstracts
An Yang, Sujian Li
Annotation corpus for discourse relations benefits NLP tasks such as machine translation and question answering. In this paper, we present SciDTB, a domain-specific discourse treeb…
Adaptations of ROUGE and BLEU to Better Evaluate Machine Reading Comprehension Task
An Yang, Kai Liu, Jing Liu +2
Current evaluation metrics to question answering based machine reading comprehension (MRC) systems generally focus on the lexical overlap between the candidate and reference answer…
Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification
Yizhong Wang, Kai Liu, Jing Liu +5
Machine reading comprehension (MRC) on real web data usually requires the machine to answer a question by analyzing multiple passages retrieved by search engine. Compared with MRC…
Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation
Shuming Ma, Xu Sun, Wei Li +3
Most recent approaches use the sequence-to-sequence model for paraphrase generation. The existing sequence-to-sequence model tends to memorize the words and the patterns in the tra…
Tag-Enhanced Tree-Structured Neural Networks for Implicit Discourse Relation Classification
Yizhong Wang, Sujian Li, Jingfeng Yang +2
Identifying implicit discourse relations between text spans is a challenging task because it requires understanding the meaning of the text. To tackle this task, recent studies hav…
Faithful to the Original: Fact Aware Neural Abstractive Summarization
Ziqiang Cao, Furu Wei, Wenjie Li +1
Unlike extractive summarization, abstractive summarization has to fuse different parts of the source text, which inclines to create fake facts. Our preliminary study reveals nearly…
Deep Stacking Networks for Low-Resource Chinese Word Segmentation with Transfer Learning
Jingjing Xu, Xu Sun, Sujian Li +2
In recent years, neural networks have proven to be effective in Chinese word segmentation. However, this promising performance relies on large-scale training data. Neural networks…
Interactive Attention Networks for Aspect-Level Sentiment Classification
Dehong Ma, Sujian Li, Xiaodong Zhang +1
Aspect-level sentiment classification aims at identifying the sentiment polarity of specific target in its context. Previous approaches have realized the importance of targets in s…
Order-Planning Neural Text Generation From Structured Data
Lei Sha, Lili Mou, Tianyu Liu +4
Generating texts from structured data (e.g., a table) is important for various natural language processing tasks such as question answering and dialog systems. In recent studies, r…
Improving Multi-Document Summarization via Text Classification
Ziqiang Cao, Wenjie Li, Sujian Li +1
Developed so far, multi-document summarization has reached its bottleneck due to the lack of sufficient training data and diverse categories of documents. Text classification just…
Joint Copying and Restricted Generation for Paraphrase
Ziqiang Cao, Chuwei Luo, Wenjie Li +1
Many natural language generation tasks, such as abstractive summarization and text simplification, are paraphrase-orientated. In these tasks, copying and rewriting are two main wri…
Recognizing Implicit Discourse Relations via Repeated Reading: Neural Networks with Multi-Level Attention
Yang Liu, Sujian Li
Recognizing implicit discourse relations is a challenging but important task in the field of Natural Language Processing. For such a complex text processing task, different from pr…
Implicit Discourse Relation Classification via Multi-Task Neural Networks
Yang Liu, Sujian Li, Xiaodong Zhang +1
Without discourse connectives, classifying implicit discourse relations is a challenging task and a bottleneck for building a practical discourse parser. Previous research usually…