101 citations · 573 across the 65 of their papers we have counts for
53 papers · 1 filter
Token-Level Fitting Issues of Seq2seq Models
Guangsheng Bao, Zhiyang Teng, Yue Zhang
Sequence-to-sequence (seq2seq) models have been widely used for natural language processing, computer vision, and other deep learning tasks. We find that seq2seq models trained wit…
A Graph Enhanced BERT Model for Event Prediction
Li Du, Xiao Ding, Yue Zhang +3
Predicting the subsequent event for an existing event context is an important but challenging task, as it requires understanding the underlying relationship between events. Previou…
Knowledgeable Salient Span Mask for Enhancing Language Models as Knowledge Base
Cunxiang Wang, Fuli Luo, Yanyang Li +3
Pre-trained language models (PLMs) like BERT have made significant progress in various downstream NLP tasks. However, by asking models to do cloze-style tests, recent work finds th…
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann +122
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained…
Solving Aspect Category Sentiment Analysis as a Text Generation Task
Jian Liu, Zhiyang Teng, Leyang Cui +2
Aspect category sentiment analysis has attracted increasing research attention. The dominant methods make use of pre-trained language models by learning effective aspect category-s…
Knowledge Enhanced Fine-Tuning for Better Handling Unseen Entities in Dialogue Generation
Leyang Cui, Yu Wu, Shujie Liu +1
Although pre-training models have achieved great success in dialogue generation, their performance drops dramatically when the input contains an entity that does not appear in pre-…
Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation
Leonardo F. R. Ribeiro, Jonas Pfeiffer, Yue Zhang +1
Recent work on multilingual AMR-to-text generation has exclusively focused on data augmentation strategies that utilize silver AMR. However, this assumes a high quality of generate…
Investigating Non-local Features for Neural Constituency Parsing
Leyang Cui, Sen Yang, Yue Zhang
Thanks to the strong representation power of neural encoders, neural chart-based parsers have achieved highly competitive performance by using local features. Recently, it has been…
Exploring Generalization Ability of Pretrained Language Models on Arithmetic and Logical Reasoning
Cunxiang Wang, Boyuan Zheng, Yuchen Niu +1
To quantitatively and intuitively explore the generalization ability of pre-trained language models (PLMs), we have designed several tasks of arithmetic and logical reasoning. We b…
DialogSum: A Real-Life Scenario Dialogue Summarization Dataset
Yulong Chen, Yang Liu, Liang Chen +1
Proposal of large-scale datasets has facilitated research on deep neural models for news summarization. Deep learning can also be potentially useful for spoken dialogue summarizati…
Template-Based Named Entity Recognition Using BART
Leyang Cui, Yu Wu, Jian Liu +2
There is a recent interest in investigating few-shot NER, where the low-resource target domain has different label sets compared with a resource-rich source domain. Existing method…
Can Generative Pre-trained Language Models Serve as Knowledge Bases for Closed-book QA?
Cunxiang Wang, Pai Liu, Yue Zhang
Recent work has investigated the interesting question using pre-trained language models (PLMs) as knowledge bases for answering open questions. However, existing work is limited in…
Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis
Linyi Yang, Jiazheng Li, Pádraig Cunningham +3
While state-of-the-art NLP models have been achieving the excellent performance of a wide range of tasks in recent years, important questions are being raised about their robustnes…
Semantic Representation for Dialogue Modeling
Xuefeng Bai, Yulong Chen, Linfeng Song +1
Although neural models have achieved competitive results in dialogue systems, they have shown limited ability in representing core semantics, such as ignoring important entities. T…
On Compositional Generalization of Neural Machine Translation
Yafu Li, Yongjing Yin, Yulong Chen +1
Modern neural machine translation (NMT) models have achieved competitive performance in standard benchmarks such as WMT. However, there still exist significant issues such as robus…
G-Transformer for Document-level Machine Translation
Guangsheng Bao, Yue Zhang, Zhiyang Teng +2
Document-level MT models are still far from satisfactory. Existing work extend translation unit from single sentence to multiple sentences. However, study shows that when we furthe…
Constrained Text Generation with Global Guidance -- Case Study on CommonGen
Yixian Liu, Liwen Zhang, Wenjuan Han +2
This paper studies constrained text generation, which is to generate sentences under certain pre-conditions. We focus on CommonGen, the task of generating text based on a set of co…
Structural Adapters in Pretrained Language Models for AMR-to-text Generation
Leonardo F. R. Ribeiro, Yue Zhang, Iryna Gurevych
Pretrained language models (PLM) have recently advanced graph-to-text generation, where the input graph is linearized into a sequence and fed into the PLM to obtain its representat…
Structural Information Preserving for Graph-to-Text Generation
Linfeng Song, Ante Wang, Jinsong Su +4
The task of graph-to-text generation aims at producing sentences that preserve the meaning of input graphs. As a crucial defect, the current state-of-the-art models may mess up or…
Contextualized Rewriting for Text Summarization
Guangsheng Bao, Yue Zhang
Extractive summarization suffers from irrelevance, redundancy and incoherence. Existing work shows that abstractive rewriting for extractive summaries can improve the conciseness a…
End-to-End Chinese Parsing Exploiting Lexicons
Yuan Zhang, Zhiyang Teng, Yue Zhang
Chinese parsing has traditionally been solved by three pipeline systems including word-segmentation, part-of-speech tagging and dependency parsing modules. In this paper, we propos…
SemGloVe: Semantic Co-occurrences for GloVe from BERT
Leilei Gan, Zhiyang Teng, Yue Zhang +3
GloVe learns word embeddings by leveraging statistical information from word co-occurrence matrices. However, word pairs in the matrices are extracted from a predefined local conte…
Natural Language Inference in Context -- Investigating Contextual Reasoning over Long Texts
Hanmeng Liu, Leyang Cui, Jian Liu +1
Natural language inference (NLI) is a fundamental NLP task, investigating the entailment relationship between two texts. Popular NLI datasets present the task at sentence-level. Wh…
Layer-Wise Multi-View Learning for Neural Machine Translation
Qiang Wang, Changliang Li, Yue Zhang +2
Traditional neural machine translation is limited to the topmost encoder layer's context representation and cannot directly perceive the lower encoder layers. Existing solutions us…