98 citations · 245 across the 28 of their papers we have counts for
8 papers · 1 filter
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…
Consecutive Question Generation via Dynamic Multitask Learning
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…
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…
IntTower: the Next Generation of Two-Tower Model for Pre-Ranking System
Xiangyang Li, Bo Chen, HuiFeng Guo +10
Scoring a large number of candidates precisely in several milliseconds is vital for industrial pre-ranking systems. Existing pre-ranking systems primarily adopt the \textbf{two-tow…
Learning Robust Representations for Continual Relation Extraction via Adversarial Class Augmentation
Peiyi Wang, Yifan Song, Tianyu Liu +4
Continual relation extraction (CRE) aims to continually learn new relations from a class-incremental data stream. CRE model usually suffers from catastrophic forgetting problem, i.…
Revising Image-Text Retrieval via Multi-Modal Entailment
Xu Yan, Chunhui Ai, Ziqiang Cao +4
An outstanding image-text retrieval model depends on high-quality labeled data. While the builders of existing image-text retrieval datasets strive to ensure that the caption match…
Low Resource Style Transfer via Domain Adaptive Meta Learning
Xiangyang Li, Xiang Long, Yu Xia +1
Text style transfer (TST) without parallel data has achieved some practical success. However, most of the existing unsupervised text style transfer methods suffer from (i) requirin…