1.2k citations · 1.5k across the 38 of their papers we have counts for
22 papers · 1 filter
Building Dialogue Understanding Models for Low-resource Language Indonesian from Scratch
Donglin Di, Weinan Zhang, Yue Zhang +1
Making use of off-the-shelf resources of resource-rich languages to transfer knowledge for low-resource languages raises much attention recently. The requirements of enabling the m…
Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation
Yulong Chen, Huajian Zhang, Yijie Zhou +8
Most existing cross-lingual summarization (CLS) work constructs CLS corpora by simply and directly translating pre-annotated summaries from one language to another, which can conta…
An AMR-based Link Prediction Approach for Document-level Event Argument Extraction
Yuqing Yang, Qipeng Guo, Xiangkun Hu +3
Recent works have introduced Abstract Meaning Representation (AMR) for Document-level Event Argument Extraction (Doc-level EAE), since AMR provides a useful interpretation of compl…
Instance Smoothed Contrastive Learning for Unsupervised Sentence Embedding
Hongliang He, Junlei Zhang, Zhenzhong Lan +1
Contrastive learning-based methods, such as unsup-SimCSE, have achieved state-of-the-art (SOTA) performances in learning unsupervised sentence embeddings. However, in previous stud…
GEMINI: Controlling the Sentence-level Writing Style for Abstractive Text Summarization
Guangsheng Bao, Zebin Ou, Yue Zhang
Human experts write summaries using different techniques, including extracting a sentence from the document and rewriting it, or fusing various information from the document to abs…
Improving (Dis)agreement Detection with Inductive Social Relation Information From Comment-Reply Interactions
Yun Luo, Zihan Liu, Stan Z. Li +1
(Dis)agreement detection aims to identify the authors' attitudes or positions (\textit{agree, disagree, neutral}) towards a specific text. It is limited for existing methods merely…
Exploiting Rich Textual User-Product Context for Improving Sentiment Analysis
Chenyang Lyu, Linyi Yang, Yue Zhang +2
User and product information associated with a review is useful for sentiment polarity prediction. Typical approaches incorporating such information focus on modeling users and pro…
DC-MBR: Distributional Cooling for Minimum Bayesian Risk Decoding
Jianhao Yan, Jin Xu, Fandong Meng +2
Minimum Bayesian Risk Decoding (MBR) emerges as a promising decoding algorithm in Neural Machine Translation. However, MBR performs poorly with label smoothing, which is surprising…
GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective
Linyi Yang, Shuibai Zhang, Libo Qin +6
Pre-trained language models (PLMs) are known to improve the generalization performance of natural language understanding models by leveraging large amounts of data during the pre-t…
Pre-Training a Graph Recurrent Network for Language Representation
Yile Wang, Linyi Yang, Zhiyang Teng +2
Transformer-based pre-trained models have gained much advance in recent years, becoming one of the most important backbones in natural language processing. Recent work shows that t…
Cross-domain Generalization for AMR Parsing
Xuefeng Bai, Seng Yang, Leyang Cui +2
Abstract Meaning Representation (AMR) parsing aims to predict an AMR graph from textual input. Recently, there has been notable growth in AMR parsing performance. However, most exi…
Multi-Granularity Optimization for Non-Autoregressive Translation
Yafu Li, Leyang Cui, Yongjing Yin +1
Despite low latency, non-autoregressive machine translation (NAT) suffers severe performance deterioration due to the naive independence assumption. This assumption is further stre…
Categorizing Semantic Representations for Neural Machine Translation
Yongjing Yin, Yafu Li, Fandong Meng +2
Modern neural machine translation (NMT) models have achieved competitive performance in standard benchmarks. However, they have recently been shown to suffer limitation in composit…
Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings
Jiangbin Zheng, Yile Wang, Ge Wang +5
Although contextualized embeddings generated from large-scale pre-trained models perform well in many tasks, traditional static embeddings (e.g., Skip-gram, Word2Vec) still play an…
Semantic-based Pre-training for Dialogue Understanding
Xuefeng Bai, Linfeng Song, Yue Zhang
Pre-trained language models have made great progress on dialogue tasks. However, these models are typically trained on surface dialogue text, thus are proven to be weak in understa…
Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect
Naihao Deng, Yulong Chen, Yue Zhang
Text-to-SQL has attracted attention from both the natural language processing and database communities because of its ability to convert the semantics in natural language into SQL…
Lost in Context? On the Sense-wise Variance of Contextualized Word Embeddings
Yile Wang, Yue Zhang
Contextualized word embeddings in language models have given much advance to NLP. Intuitively, sentential information is integrated into the representation of words, which can help…
A General Contextualized Rewriting Framework for Text Summarization
Guangsheng Bao, Yue Zhang
The rewriting method for text summarization combines extractive and abstractive approaches, improving the conciseness and readability of extractive summaries using an abstractive m…
The Cross-lingual Conversation Summarization Challenge
Yulong Chen, Ming Zhong, Xuefeng Bai +4
We propose the shared task of cross-lingual conversation summarization, \emph{ConvSumX Challenge}, opening new avenues for researchers to investigate solutions that integrate conve…
Towards Robust Online Dialogue Response Generation
Leyang Cui, Fandong Meng, Yijin Liu +2
Although pre-trained sequence-to-sequence models have achieved great success in dialogue response generation, chatbots still suffer from generating inconsistent responses in real-w…
Do Prompts Solve NLP Tasks Using Natural Language?
Sen Yang, Yunchen Zhang, Leyang Cui +1
Thanks to the advanced improvement of large pre-trained language models, prompt-based fine-tuning is shown to be effective on a variety of downstream tasks. Though many prompting m…
Graph Pre-training for AMR Parsing and Generation
Xuefeng Bai, Yulong Chen, Yue Zhang
Abstract meaning representation (AMR) highlights the core semantic information of text in a graph structure. Recently, pre-trained language models (PLMs) have advanced tasks of AMR…