416 citations · 1k across the 56 of their papers we have counts for
41 papers · 1 filter
Hallucination Self-Play: Bootstrapping Reinforced Detector via Evolved Generator
Shiping Yang, Shining Liang, Weihao Liu +4
Identifying faithfulness hallucinations in LLM-generated outputs remains challenging due to the scarcity of high-quality annotated data. Recent work relies on advanced LLMs to synt…
Hypertext Entity Extraction in Webpage
Yifei Yang, Tianqiao Liu, Bo Shao +4
Webpage entity extraction is a fundamental natural language processing task in both research and applications. Nowadays, the majority of webpage entity extraction models are traine…
Is Bigger and Deeper Always Better? Probing LLaMA Across Scales and Layers
Nuo Chen, Ning Wu, Shining Liang +4
This paper presents an in-depth analysis of Large Language Models (LLMs), focusing on LLaMA, a prominent open-source foundational model in natural language processing. Instead of a…
Coherent Entity Disambiguation via Modeling Topic and Categorical Dependency
Zilin Xiao, Linjun Shou, Xingyao Zhang +4
Previous entity disambiguation (ED) methods adopt a discriminative paradigm, where prediction is made based on matching scores between mention context and candidate entities using…
Instructed Language Models with Retrievers Are Powerful Entity Linkers
Zilin Xiao, Ming Gong, Jie Wu +4
Generative approaches powered by large language models (LLMs) have demonstrated emergent abilities in tasks that require complex reasoning abilities. Yet the generative nature stil…
Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations
Nuo Chen, Zinan Zheng, Ning Wu +3
Existing research predominantly focuses on developing powerful language learning models (LLMs) for mathematical reasoning within monolingual languages, with few explorations in pre…
Alleviating Over-smoothing for Unsupervised Sentence Representation
Nuo Chen, Linjun Shou, Ming Gong +5
Currently, learning better unsupervised sentence representations is the pursuit of many natural language processing communities. Lots of approaches based on pre-trained language mo…
Large Language Models are Diverse Role-Players for Summarization Evaluation
Ning Wu, Ming Gong, Linjun Shou +2
Text summarization has a wide range of applications in many scenarios. The evaluation of the quality of the generated text is a complex problem. A big challenge to language evaluat…
Bridge the Gap between Language models and Tabular Understanding
Nuo Chen, Linjun Shou, Ming Gong +5
Table pretrain-then-finetune paradigm has been proposed and employed at a rapid pace after the success of pre-training in the natural language domain. Despite the promising finding…
Label-aware Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding
Shining Liang, Linjun Shou, Jian Pei +4
Despite the great success of spoken language understanding (SLU) in high-resource languages, it remains challenging in low-resource languages mainly due to the lack of labeled trai…
Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling
Nuo Chen, Linjun Shou, Ming Gong +2
Large-scale cross-lingual pre-trained language models (xPLMs) have shown effectiveness in cross-lingual sequence labeling tasks (xSL), such as cross-lingual machine reading compreh…
Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding
Yingmei Guo, Linjun Shou, Jian Pei +4
Lack of training data presents a grand challenge to scaling out spoken language understanding (SLU) to low-resource languages. Although various data augmentation approaches have be…
A Joint and Domain-Adaptive Approach to Spoken Language Understanding
Linhao Zhang, Yu Shi, Linjun Shou +3
Spoken Language Understanding (SLU) is composed of two subtasks: intent detection (ID) and slot filling (SF). There are two lines of research on SLU. One jointly tackles these two…
Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition
Shining Liang, Ming Gong, Jian Pei +4
Named entity recognition (NER) is a fundamental component in many applications, such as Web Search and Voice Assistants. Although deep neural networks greatly improve the performan…
CoSQA: 20,000+ Web Queries for Code Search and Question Answering
Junjie Huang, Duyu Tang, Linjun Shou +5
Finding codes given natural language query isb eneficial to the productivity of software developers. Future progress towards better semantic matching between query and code require…
Retrieval Enhanced Model for Commonsense Generation
Han Wang, Yang Liu, Chenguang Zhu +4
Commonsense generation is a challenging task of generating a plausible sentence describing an everyday scenario using provided concepts. Its requirement of reasoning over commonsen…
WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach
Junjie Huang, Duyu Tang, Wanjun Zhong +5
Producing the embedding of a sentence in an unsupervised way is valuable to natural language matching and retrieval problems in practice. In this work, we conduct a thorough examin…
Generating Human Readable Transcript for Automatic Speech Recognition with Pre-trained Language Model
Junwei Liao, Yu Shi, Ming Gong +5
Modern Automatic Speech Recognition (ASR) systems can achieve high performance in terms of recognition accuracy. However, a perfectly accurate transcript still can be challenging t…
Improving Zero-shot Neural Machine Translation on Language-specific Encoders-Decoders
Junwei Liao, Yu Shi, Ming Gong +3
Recently, universal neural machine translation (NMT) with shared encoder-decoder gained good performance on zero-shot translation. Unlike universal NMT, jointly trained language-sp…
Reinforced Multi-Teacher Selection for Knowledge Distillation
Fei Yuan, Linjun Shou, Jian Pei +4
In natural language processing (NLP) tasks, slow inference speed and huge footprints in GPU usage remain the bottleneck of applying pre-trained deep models in production. As a popu…
Syntax-Enhanced Pre-trained Model
Zenan Xu, Daya Guo, Duyu Tang +7
We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre…
CalibreNet: Calibration Networks for Multilingual Sequence Labeling
Shining Liang, Linjun Shou, Jian Pei +3
Lack of training data in low-resource languages presents huge challenges to sequence labeling tasks such as named entity recognition (NER) and machine reading comprehension (MRC).…
GLGE: A New General Language Generation Evaluation Benchmark
Dayiheng Liu, Yu Yan, Yeyun Gong +15
Multi-task benchmarks such as GLUE and SuperGLUE have driven great progress of pretraining and transfer learning in Natural Language Processing (NLP). These benchmarks mostly focus…
Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation
Junhao Liu, Linjun Shou, Jian Pei +3
Cross-lingual Machine Reading Comprehension (CLMRC) remains a challenging problem due to the lack of large-scale annotated datasets in low-source languages, such as Arabic, Hindi,…