Search Results for author: ShuJian Huang

Found 35 papers, 17 papers with code

Data Augmentation for Low-resource Word Segmentation and POS Tagging of Ancient Chinese Texts

no code implementations LT4HALA (LREC) 2022 Yutong Shen, Jiahuan Li, ShuJian Huang, Yi Zhou, Xiaopeng Xie, Qinxin Zhao

Although SikuRoberta significantly boosts performance on WSG and POS tasks on ancient Chinese texts, the lack of labeled data still limits the performance of the model.

Data Augmentation Language Modelling +2

Meta-LMTC: Meta-Learning for Large-Scale Multi-Label Text Classification

no code implementations EMNLP 2021 Ran Wang, Xi’ao Su, Siyu Long, Xinyu Dai, ShuJian Huang, Jiajun Chen

However, the simple extension of meta-learning approaches to multi-label classification is sub-optimal for LMTC tasks due to long-tailed label distribution and coexisting of few- and zero-shot scenarios.

Meta-Learning Multi-Label Classification +3

NJU’s submission to the WMT20 QE Shared Task

no code implementations WMT (EMNLP) 2020 Qu Cui, Xiang Geng, ShuJian Huang, Jiajun Chen

This paper describes our system of the sentence-level and word-level Quality Estimation Shared Task of WMT20.

Language Modelling

GLAT: Glancing at Latent Variables for Parallel Text Generation

1 code implementation ACL 2022 Yu Bao, Hao Zhou, ShuJian Huang, Dongqi Wang, Lihua Qian, Xinyu Dai, Jiajun Chen, Lei LI

Recently, parallel text generation has received widespread attention due to its success in generation efficiency.

Text Generation

Learning from Adjective-Noun Pairs: A Knowledge-enhanced Framework for Target-Oriented Multimodal Sentiment Classification

1 code implementation COLING 2022 Fei Zhao, Zhen Wu, Siyu Long, Xinyu Dai, ShuJian Huang, Jiajun Chen

Target-oriented multimodal sentiment classification (TMSC) is a new subtask of aspect-based sentiment analysis, which aims to determine the sentiment polarity of the opinion target mentioned in a (sentence, image) pair.

Aspect-Based Sentiment Analysis

Towards Multi-label Unknown Intent Detection

1 code implementation COLING 2022 Yawen Ouyang, Zhen Wu, Xinyu Dai, ShuJian Huang, Jiajun Chen

In this paper, we propose a more desirable task, multi-label unknown intent detection, to detect whether the utterance contains the unknown intent, in which each utterance may contain multiple intents.

Intent Detection

CoP: Factual Inconsistency Detection by Controlling the Preference

1 code implementation3 Dec 2022 Shuaijie She, Xiang Geng, ShuJian Huang, Jiajun Chen

To separate the preference for factual consistency, we propose an unsupervised framework named CoP by controlling the preference of the generation model with the help of prompt.

Abstractive Text Summarization

Helping the Weak Makes You Strong: Simple Multi-Task Learning Improves Non-Autoregressive Translators

no code implementations11 Nov 2022 Xinyou Wang, Zaixiang Zheng, ShuJian Huang

Recently, non-autoregressive (NAR) neural machine translation models have received increasing attention due to their efficient parallel decoding.

Machine Translation Multi-Task Learning

What Knowledge Is Needed? Towards Explainable Memory for kNN-MT Domain Adaptation

no code implementations8 Nov 2022 Wenhao Zhu, ShuJian Huang, Yunzhe Lv, Xin Zheng, Jiajun Chen

kNN-MT presents a new paradigm for domain adaptation by building an external datastore, which usually saves all target language token occurrences in the parallel corpus.

Domain Adaptation NMT +1

Structure-Unified M-Tree Coding Solver for MathWord Problem

1 code implementation22 Oct 2022 Bin Wang, Jiangzhou Ju, Yang Fan, Xinyu Dai, ShuJian Huang, Jiajun Chen

As one of the challenging NLP tasks, designing math word problem (MWP) solvers has attracted increasing research attention for the past few years.

Probing Cross-modal Semantics Alignment Capability from the Textual Perspective

no code implementations18 Oct 2022 Zheng Ma, Shi Zong, Mianzhi Pan, Jianbing Zhang, ShuJian Huang, Xinyu Dai, Jiajun Chen

In recent years, vision and language pre-training (VLP) models have advanced the state-of-the-art results in a variety of cross-modal downstream tasks.

Image Captioning

A Numerical Reasoning Question Answering System with Fine-grained Retriever and the Ensemble of Multiple Generators for FinQA

no code implementations17 Jun 2022 Bin Wang, Jiangzhou Ju, Yunlin Mao, Xin-yu Dai, ShuJian Huang, Jiajun Chen

Here, we propose a numerical reasoning question answering system to answer numerical reasoning questions among financial text and table data sources, consisting of a retriever module, a generator module, and an ensemble module.

Question Answering

Analyzing the Intensity of Complaints on Social Media

1 code implementation Findings (NAACL) 2022 Ming Fang, Shi Zong, Jing Li, Xinyu Dai, ShuJian Huang, Jiajun Chen

Furthermore, we conduct a comprehensive linguistic analysis around complaints, including the connections between complaints and sentiment, and a cross-lingual comparison for complaints expressions used by Chinese and English speakers.

$\textit{latent}$-GLAT: Glancing at Latent Variables for Parallel Text Generation

1 code implementation5 Apr 2022 Yu Bao, Hao Zhou, ShuJian Huang, Dongqi Wang, Lihua Qian, Xinyu Dai, Jiajun Chen, Lei LI

Recently, parallel text generation has received widespread attention due to its success in generation efficiency.

Text Generation

Non-Parametric Online Learning from Human Feedback for Neural Machine Translation

1 code implementation23 Sep 2021 Dongqi Wang, Haoran Wei, Zhirui Zhang, ShuJian Huang, Jun Xie, Jiajun Chen

We study the problem of online learning with human feedback in the human-in-the-loop machine translation, in which the human translators revise the machine-generated translations and then the corrected translations are used to improve the neural machine translation (NMT) system.

Machine Translation NMT +1

Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation

1 code implementation Findings (EMNLP) 2021 Xin Zheng, Zhirui Zhang, ShuJian Huang, Boxing Chen, Jun Xie, Weihua Luo, Jiajun Chen

Recently, $k$NN-MT has shown the promising capability of directly incorporating the pre-trained neural machine translation (NMT) model with domain-specific token-level $k$-nearest-neighbor ($k$NN) retrieval to achieve domain adaptation without retraining.

Machine Translation NMT +3

Energy-based Unknown Intent Detection with Data Manipulation

2 code implementations Findings (ACL) 2021 Yawen Ouyang, Jiasheng Ye, Yu Chen, Xinyu Dai, ShuJian Huang, Jiajun Chen

Unknown intent detection aims to identify the out-of-distribution (OOD) utterance whose intent has never appeared in the training set.

Intent Detection

Adaptive Nearest Neighbor Machine Translation

2 code implementations ACL 2021 Xin Zheng, Zhirui Zhang, Junliang Guo, ShuJian Huang, Boxing Chen, Weihua Luo, Jiajun Chen

On four benchmark machine translation datasets, we demonstrate that the proposed method is able to effectively filter out the noises in retrieval results and significantly outperforms the vanilla kNN-MT model.

Machine Translation NMT +2

DirectQE: Direct Pretraining for Machine Translation Quality Estimation

no code implementations15 May 2021 Qu Cui, ShuJian Huang, Jiahuan Li, Xiang Geng, Zaixiang Zheng, Guoping Huang, Jiajun Chen

However, we argue that there are gaps between the predictor and the estimator in both data quality and training objectives, which preclude QE models from benefiting from a large number of parallel corpora more directly.

Machine Translation Translation

Dual Side Deep Context-aware Modulation for Social Recommendation

1 code implementation16 Mar 2021 Bairan Fu, Wenming Zhang, GuangNeng Hu, Xinyu Dai, ShuJian Huang, Jiajun Chen

Specifically, we first proposed a novel graph neural network to model the social relation and collaborative relation, and on top of high-order relations, a dual side deep context-aware modulation is introduced to capture the friends' information and item attraction.

FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation

1 code implementation LREC 2022 Wenhao Zhu, ShuJian Huang, Tong Pu, Pingxuan Huang, Xu Zhang, Jian Yu, Wei Chen, Yanfeng Wang, Jiajun Chen

Previous research for adapting a general neural machine translation (NMT) model into a specific domain usually neglects the diversity in translation within the same domain, which is a core problem for domain adaptation in real-world scenarios.

Autonomous Vehicles Domain Adaptation +3

A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction

no code implementations COLING 2020 Yanyang Li, Yingfeng Luo, Ye Lin, Quan Du, Huizhen Wang, ShuJian Huang, Tong Xiao, Jingbo Zhu

Our experiments show that this simple method does not hamper the performance of similar language pairs and achieves an accuracy of 13. 64~55. 53% between English and four distant languages, i. e., Chinese, Japanese, Vietnamese and Thai.

Dimensionality Reduction Self-Learning

Opinion Transmission Network for Jointly Improving Aspect-oriented Opinion Words Extraction and Sentiment Classification

no code implementations1 Nov 2020 Chengcan Ying, Zhen Wu, Xinyu Dai, ShuJian Huang, Jiajun Chen

In this paper, we propose a novel joint model, Opinion Transmission Network (OTN), to exploit the potential bridge between ALSC and AOWE to achieve the goal of facilitating them simultaneously.

Aspect-Based Sentiment Analysis General Classification

Transformer-based Multi-Aspect Modeling for Multi-Aspect Multi-Sentiment Analysis

no code implementations1 Nov 2020 Zhen Wu, Chengcan Ying, Xinyu Dai, ShuJian Huang, Jiajun Chen

To facilitate the research of ABSA, NLPCC 2020 Shared Task 2 releases a new large-scale Multi-Aspect Multi-Sentiment (MAMS) dataset.

Aspect-Based Sentiment Analysis

Non-linear Learning for Statistical Machine Translation

no code implementations IJCNLP 2015 Shujian Huang, Huadong Chen, Xin-yu Dai, Jia-Jun Chen

The linear combination assumes that all the features are in a linear relationship and constrains that each feature interacts with the rest features in an linear manner, which might limit the expressive power of the model and lead to a under-fit model on the current data.

Machine Translation Translation

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