1.2k citations · 1.5k across the 38 of their papers we have counts for
19 papers · 1 filter
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…
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report
Marcos V. Conde, Radu Timofte, Yibin Huang +40
Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP). Numerous low-level…
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…
A Rationale-Centric Framework for Human-in-the-loop Machine Learning
Jinghui Lu, Linyi Yang, Brian Mac Namee +1
We present a novel rationale-centric framework with human-in-the-loop -- Rationales-centric Double-robustness Learning (RDL) -- to boost model out-of-distribution performance in fe…
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…
Revisiting QMIX: Discriminative Credit Assignment by Gradient Entropy Regularization
Jian Zhao, Yue Zhang, Xunhan Hu +5
In cooperative multi-agent systems, agents jointly take actions and receive a team reward instead of individual rewards. In the absence of individual reward signals, credit assignm…