Search Results for author: Jingjing Xu

Found 50 papers, 25 papers with code

Improving And Analyzing Neural Speaker Embeddings for ASR

no code implementations11 Jan 2023 Christoph Lüscher, Jingjing Xu, Mohammad Zeineldeen, Ralf Schlüter, Hermann Ney

By further adding neural speaker embeddings, we gain additional ~3% relative WER improvement on Hub5'00.

Speaker Verification

A Survey for In-context Learning

1 code implementation31 Dec 2022 Qingxiu Dong, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu sun, Jingjing Xu, Lei LI, Zhifang Sui

With the increasing ability of large language models (LLMs), in-context learning (ICL) has become a new paradigm for natural language processing (NLP), where LLMs make predictions only based on contexts augmented with a few training examples.

Go-tuning: Improving Zero-shot Learning Abilities of Smaller Language Models

no code implementations20 Dec 2022 Jingjing Xu, Qingxiu Dong, Hongyi Liu, Lei LI

With increasing scale, large language models demonstrate both quantitative improvement and new qualitative capabilities, especially as zero-shot learners, like GPT-3.

Language Modelling Masked Language Modeling +2

BigText-QA: Question Answering over a Large-Scale Hybrid Knowledge Graph

no code implementations12 Dec 2022 Jingjing Xu, Maria Biryukov, Martin Theobald, Vinu Ellampallil Venugopal

Answering complex questions over textual resources remains a challenging problem$\unicode{x2013}$especially when interpreting the fine-grained relationships among multiple entities that occur within a natural-language question or clue.

Question Answering

Enhancing and Adversarial: Improve ASR with Speaker Labels

no code implementations11 Nov 2022 Wei Zhou, Haotian Wu, Jingjing Xu, Mohammad Zeineldeen, Christoph Lüscher, Ralf Schlüter, Hermann Ney

Detailed analysis and experimental verification are conducted to show the optimal positions in the ASR neural network (NN) to apply speaker enhancing and adversarial training.

Multi-Task Learning

Improving the Training Recipe for a Robust Conformer-based Hybrid Model

no code implementations26 Jun 2022 Mohammad Zeineldeen, Jingjing Xu, Christoph Lüscher, Ralf Schlüter, Hermann Ney

In this work, we investigate various methods for speaker adaptive training (SAT) based on feature-space approaches for a conformer-based acoustic model (AM) on the Switchboard 300h dataset.

Automatic Speech Recognition speech-recognition

KNAS: Green Neural Architecture Search

1 code implementation26 Nov 2021 Jingjing Xu, Liang Zhao, Junyang Lin, Rundong Gao, Xu sun, Hongxia Yang

Many existing neural architecture search (NAS) solutions rely on downstream training for architecture evaluation, which takes enormous computations.

Image Classification Neural Architecture Search +2

A Survey on Green Deep Learning

no code implementations8 Nov 2021 Jingjing Xu, Wangchunshu Zhou, Zhiyi Fu, Hao Zhou, Lei LI

In recent years, larger and deeper models are springing up and continuously pushing state-of-the-art (SOTA) results across various fields like natural language processing (NLP) and computer vision (CV).

Knowledge Distillation Model Compression

Conformer-based Hybrid ASR System for Switchboard Dataset

no code implementations5 Nov 2021 Mohammad Zeineldeen, Jingjing Xu, Christoph Lüscher, Wilfried Michel, Alexander Gerstenberger, Ralf Schlüter, Hermann Ney

The recently proposed conformer architecture has been successfully used for end-to-end automatic speech recognition (ASR) architectures achieving state-of-the-art performance on different datasets.

Automatic Speech Recognition speech-recognition

A Gradient-based Kernel Approach for Efficient Network Architecture Search

no code implementations1 Jan 2021 Jingjing Xu, Liang Zhao, Junyang Lin, Xu sun, Hongxia Yang

Inspired by our new finding, we explore a simple yet effective network architecture search (NAS) approach that leverages gradient correlation and gradient values to find well-performing architectures.

Image Classification text-classification +1

Information-theoretic Vocabularization via Optimal Transport

no code implementations1 Jan 2021 Jingjing Xu, Hao Zhou, Chun Gan, Zaixiang Zheng, Lei LI

In this paper, we find an exciting relation between an information-theoretic feature and the performance of NLP tasks such as machine translation with a given vocabulary.

Machine Translation Translation

Graph-based Multi-hop Reasoning for Long Text Generation

no code implementations28 Sep 2020 Liang Zhao, Jingjing Xu, Junyang Lin, Yichang Zhang, Hongxia Yang, Xu sun

The reasoning module is responsible for searching skeleton paths from a knowledge graph to imitate the imagination process in the human writing for semantic transfer.

Review Generation Story Generation

MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning

2 code implementations17 Nov 2019 Guangxiang Zhao, Xu sun, Jingjing Xu, Zhiyuan Zhang, Liangchen Luo

In this work, we explore parallel multi-scale representation learning on sequence data, striving to capture both long-range and short-range language structures.

Machine Translation Representation Learning +1

Understanding and Improving Layer Normalization

1 code implementation NeurIPS 2019 Jingjing Xu, Xu sun, Zhiyuan Zhang, Guangxiang Zhao, Junyang Lin

Unlike them, we find that the derivatives of the mean and variance are more important than forward normalization by re-centering and re-scaling backward gradients.

Machine Translation Translation

Specificity-Driven Cascading Approach for Unsupervised Sentiment Modification

no code implementations IJCNLP 2019 Pengcheng Yang, Junyang Lin, Jingjing Xu, Jun Xie, Qi Su, Xu sun

The task of unsupervised sentiment modification aims to reverse the sentiment polarity of the input text while preserving its semantic content without any parallel data.


Reasoning Over Semantic-Level Graph for Fact Checking

1 code implementation ACL 2020 Wanjun Zhong, Jingjing Xu, Duyu Tang, Zenan Xu, Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin

We evaluate our system on FEVER, a benchmark dataset for fact checking, and find that rich structural information is helpful and both our graph-based mechanisms improve the accuracy.

Claim Verification Fact Checking +3

Coherent Comments Generation for Chinese Articles with a Graph-to-Sequence Model

1 code implementation ACL 2019 Wei Li, Jingjing Xu, Yancheng He, ShengLi Yan, Yunfang Wu, Xu sun

In this paper, we propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph.


PKUSEG: A Toolkit for Multi-Domain Chinese Word Segmentation

3 code implementations27 Jun 2019 Ruixuan Luo, Jingjing Xu, Yi Zhang, Zhiyuan Zhang, Xuancheng Ren, Xu sun

Through this method, we generate synthetic data using a large amount of unlabeled data in the target domain and then obtain a word segmentation model for the target domain.

Chinese Word Segmentation Domain Adaptation +1

Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model

1 code implementation4 Jun 2019 Wei Li, Jingjing Xu, Yancheng He, ShengLi Yan, Yunfang Wu, Xu sun

In this paper, we propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph.


Learning Unsupervised Word Mapping by Maximizing Mean Discrepancy

no code implementations1 Nov 2018 Pengcheng Yang, Fuli Luo, Shuangzhi Wu, Jingjing Xu, Dong-dong Zhang, Xu sun

In order to avoid such sophisticated alternate optimization, we propose to learn unsupervised word mapping by directly maximizing the mean discrepancy between the distribution of transferred embedding and target embedding.

Cross-Lingual Word Embeddings Density Estimation +4

Evaluating Semantic Rationality of a Sentence: A Sememe-Word-Matching Neural Network based on HowNet

no code implementations11 Sep 2018 Shu Liu, Jingjing Xu, Xuancheng Ren, Xu sun

To evaluate the effectiveness of the proposed model, we build a large-scale rationality evaluation dataset.

Language Modelling

An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation

1 code implementation EMNLP 2018 Liangchen Luo, Jingjing Xu, Junyang Lin, Qi Zeng, Xu sun

Different from conventional text generation tasks, the mapping between inputs and responses in conversations is more complicated, which highly demands the understanding of utterance-level semantic dependency, a relation between the whole meanings of inputs and outputs.

Dialogue Generation

Learning Sentiment Memories for Sentiment Modification without Parallel Data

1 code implementation EMNLP 2018 Yi Zhang, Jingjing Xu, Pengcheng Yang, Xu sun

The task of sentiment modification requires reversing the sentiment of the input and preserving the sentiment-independent content.

Text Style Transfer

A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation

1 code implementation EMNLP 2018 Jingjing Xu, Xuancheng Ren, Yi Zhang, Qi Zeng, Xiaoyan Cai, Xu sun

Compared to the state-of-the-art models, our skeleton-based model can generate significantly more coherent text according to human evaluation and automatic evaluation.

Story Generation

Primal Meaning Recommendation via On-line Encyclopedia

no code implementations14 Aug 2018 Zhiyuan Zhang, Wei Li, Jingjing Xu, Xu sun

We define the primal meaning of an expression to be a frequently used sense of that expression from which its other frequent senses can be deduced.

A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text

2 code implementations19 Nov 2017 Jingjing Xu, Ji Wen, Xu sun, Qi Su

To build a high quality dataset, we propose two tagging methods to solve the problem of data inconsistency, including a heuristic tagging method and a machine auxiliary tagging method.

named-entity-recognition NER +1

Training Simplification and Model Simplification for Deep Learning: A Minimal Effort Back Propagation Method

3 code implementations17 Nov 2017 Xu Sun, Xuancheng Ren, Shuming Ma, Bingzhen Wei, Wei Li, Jingjing Xu, Houfeng Wang, Yi Zhang

Based on the sparsified gradients, we further simplify the model by eliminating the rows or columns that are seldom updated, which will reduce the computational cost both in the training and decoding, and potentially accelerate decoding in real-world applications.

Deep Stacking Networks for Low-Resource Chinese Word Segmentation with Transfer Learning

no code implementations4 Nov 2017 Jingjing Xu, Xu sun, Sujian Li, Xiaoyan Cai, Bingzhen Wei

In this paper, we propose a deep stacking framework to improve the performance on word segmentation tasks with insufficient data by integrating datasets from diverse domains.

Chinese Word Segmentation Transfer Learning

Improving Social Media Text Summarization by Learning Sentence Weight Distribution

no code implementations31 Oct 2017 Jingjing Xu

Recently, encoder-decoder models are widely used in social media text summarization.

Text Summarization

Shallow Discourse Parsing with Maximum Entropy Model

no code implementations31 Oct 2017 Jingjing Xu

The head-based representation of the PDTB is adopted in the arguments identifier, which turns the problem of indentifying the arguments of discourse connective into finding the head and end of the arguments.

Discourse Parsing

Minimal Effort Back Propagation for Convolutional Neural Networks

no code implementations18 Sep 2017 Bingzhen Wei, Xu sun, Xuancheng Ren, Jingjing Xu

As traditional neural network consumes a significant amount of computing resources during back propagation, \citet{Sun2017mePropSB} propose a simple yet effective technique to alleviate this problem.

Transfer Deep Learning for Low-Resource Chinese Word Segmentation with a Novel Neural Network

no code implementations15 Feb 2017 Jingjing Xu, Xu sun

First, we train a teacher model on high-resource corpora and then use the learned knowledge to initialize a student model.

Chinese Word Segmentation Transfer Learning

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