50 citations · 219 across the 30 of their papers we have counts for
26 papers · 1 filter
UNIMO-3: Multi-granularity Interaction for Vision-Language Representation Learning
Hao Yang, Can Gao, Hao Líu +3
Vision-and-language (VL) pre-training, which aims to learn a general representation of image-text pairs that can be transferred to various vision-and-language tasks. Compared with…
WeCheck: Strong Factual Consistency Checker via Weakly Supervised Learning
Wenhao Wu, Wei Li, Xinyan Xiao +3
A crucial issue of current text generation models is that they often uncontrollably generate factually inconsistent text with respective of their inputs. Limited by the lack of ann…
FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness
Wenhao Wu, Wei Li, Jiachen Liu +4
Despite being able to generate fluent and grammatical text, current Seq2Seq summarization models still suffering from the unfaithful generation problem. In this paper, we study the…
Precisely the Point: Adversarial Augmentations for Faithful and Informative Text Generation
Wenhao Wu, Wei Li, Jiachen Liu +3
Though model robustness has been extensively studied in language understanding, the robustness of Seq2Seq generation remains understudied. In this paper, we conduct the first quant…
SeSQL: Yet Another Large-scale Session-level Chinese Text-to-SQL Dataset
Saihao Huang, Lijie Wang, Zhenghua Li +6
As the first session-level Chinese dataset, CHASE contains two separate parts, i.e., 2,003 sessions manually constructed from scratch (CHASE-C), and 3,456 sessions translated from…
An Interpretability Evaluation Benchmark for Pre-trained Language Models
Yaozong Shen, Lijie Wang, Ying Chen +3
While pre-trained language models (LMs) have brought great improvements in many NLP tasks, there is increasing attention to explore capabilities of LMs and interpret their predicti…
A Fine-grained Interpretability Evaluation Benchmark for Neural NLP
Lijie Wang, Yaozong Shen, Shuyuan Peng +7
While there is increasing concern about the interpretability of neural models, the evaluation of interpretability remains an open problem, due to the lack of proper evaluation data…
Faster and Better Grammar-based Text-to-SQL Parsing via Clause-level Parallel Decoding and Alignment Loss
Kun Wu, Lijie Wang, Zhenghua Li +1
Grammar-based parsers have achieved high performance in the cross-domain text-to-SQL parsing task, but suffer from low decoding efficiency due to the much larger number of actions…
Unified Structure Generation for Universal Information Extraction
Yaojie Lu, Qing Liu, Dai Dai +5
Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas. In this paper, we propose a unified text-to-structure generation fra…
PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation
Zhe Hu, Hou Pong Chan, Jiachen Liu +3
Despite recent progress of pre-trained language models on generating fluent text, existing methods still suffer from incoherence problems in long-form text generation tasks that re…
Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods
Wei Li, Wenhao Wu, Moye Chen +3
Natural Language Generation (NLG) has made great progress in recent years due to the development of deep learning techniques such as pre-trained language models. This advancement h…
SgSum: Transforming Multi-document Summarization into Sub-graph Selection
Moye Chen, Wei Li, Jiachen Liu +3
Most of existing extractive multi-document summarization (MDS) methods score each sentence individually and extract salient sentences one by one to compose a summary, which have tw…
A Multimodal Sentiment Dataset for Video Recommendation
Hongxuan Tang, Hao Liu, Xinyan Xiao +1
Recently, multimodal sentiment analysis has seen remarkable advance and a lot of datasets are proposed for its development. In general, current multimodal sentiment analysis datase…
Fine-grained Entity Typing via Label Reasoning
Qing Liu, Hongyu Lin, Xinyan Xiao +3
Conventional entity typing approaches are based on independent classification paradigms, which make them difficult to recognize inter-dependent, long-tailed and fine-grained entity…
DuTrust: A Sentiment Analysis Dataset for Trustworthiness Evaluation
Lijie Wang, Hao Liu, Shuyuan Peng +5
While deep learning models have greatly improved the performance of most artificial intelligence tasks, they are often criticized to be untrustworthy due to the black-box problem.…
Controllable Dialogue Generation with Disentangled Multi-grained Style Specification and Attribute Consistency Reward
Zhe Hu, Zhiwei Cao, Hou Pong Chan +4
Controllable text generation is an appealing but challenging task, which allows users to specify particular attributes of the generated outputs. In this paper, we propose a control…
BASS: Boosting Abstractive Summarization with Unified Semantic Graph
Wenhao Wu, Wei Li, Xinyan Xiao +5
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.…
Syntactic and Semantic-driven Learning for Open Information Extraction
Jialong Tang, Yaojie Lu, Hongyu Lin +4
One of the biggest bottlenecks in building accurate, high coverage neural open IE systems is the need for large labelled corpora. The diversity of open domain corpora and the varie…
Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing
Kun Wu, Lijie Wang, Zhenghua Li +5
Data augmentation has attracted a lot of research attention in the deep learning era for its ability in alleviating data sparseness. The lack of labeled data for unseen evaluation…
UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning
Wei Li, Can Gao, Guocheng Niu +5
Existed pre-training methods either focus on single-modal tasks or multi-modal tasks, and cannot effectively adapt to each other. They can only utilize single-modal data (i.e. text…
A Practical Chinese Dependency Parser Based on A Large-scale Dataset
Shuai Zhang, Lijie Wang, Ke Sun +1
Dependency parsing is a longstanding natural language processing task, with its outputs crucial to various downstream tasks. Recently, neural network based (NN-based) dependency pa…
Leveraging Graph to Improve Abstractive Multi-Document Summarization
Wei Li, Xinyan Xiao, Jiachen Liu +3
Graphs that capture relations between textual units have great benefits for detecting salient information from multiple documents and generating overall coherent summaries. In this…
SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis
Hao Tian, Can Gao, Xinyan Xiao +5
Recently, sentiment analysis has seen remarkable advance with the help of pre-training approaches. However, sentiment knowledge, such as sentiment words and aspect-sentiment pairs,…
Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer
Chulun Zhou, Liangyu Chen, Jiachen Liu +4
Unsupervised style transfer aims to change the style of an input sentence while preserving its original content without using parallel training data. In current dominant approaches…