Search Results for author: Tiejun Zhao

Found 64 papers, 10 papers with code

A Neural Conversation Generation Model via Equivalent Shared Memory Investigation

1 code implementation20 Aug 2021 Changzhen Ji, Yating Zhang, Xiaozhong Liu, Adam Jatowt, Changlong Sun, Conghui Zhu, Tiejun Zhao

Nevertheless, few works utilized the knowledge extracted from similar conversations for utterance generation.

Text Generation

Issues with Entailment-based Zero-shot Text Classification

1 code implementation ACL 2021 Tingting Ma, Jin-Ge Yao, Chin-Yew Lin, Tiejun Zhao

The general format of natural language inference (NLI) makes it tempting to be used for zero-shot text classification by casting any target label into a sentence of hypothesis and verifying whether or not it could be entailed by the input, aiming at generic classification applicable on any specified label space.

Classification Natural Language Inference +1

Discriminative Reasoning for Document-level Relation Extraction

1 code implementation3 Jun 2021 Wang Xu, Kehai Chen, Tiejun Zhao

Document-level relation extraction (DocRE) models generally use graph networks to implicitly model the reasoning skill (i. e., pattern recognition, logical reasoning, coreference reasoning, etc.)

Relation Extraction

Gumbel-Attention for Multi-modal Machine Translation

no code implementations16 Mar 2021 Pengbo Liu, Hailong Cao, Tiejun Zhao

Through the score matrix of Gumbel-Attention and image features, the image-aware text representation is generated.

Multimodal Machine Translation Translation

Document-Level Relation Extraction with Reconstruction

1 code implementation21 Dec 2020 Wang Xu, Kehai Chen, Tiejun Zhao

In document-level relation extraction (DocRE), graph structure is generally used to encode relation information in the input document to classify the relation category between each entity pair, and has greatly advanced the DocRE task over the past several years.

Document-level Relation Classification

Robust Machine Reading Comprehension by Learning Soft labels

no code implementations COLING 2020 Zhenyu Zhao, Shuangzhi Wu, Muyun Yang, Kehai Chen, Tiejun Zhao

Neural models have achieved great success on the task of machine reading comprehension (MRC), which are typically trained on hard labels.

Machine Reading Comprehension

Cross Copy Network for Dialogue Generation

1 code implementation EMNLP 2020 Changzhen Ji, Xin Zhou, Yating Zhang, Xiaozhong Liu, Changlong Sun, Conghui Zhu, Tiejun Zhao

In the past few years, audiences from different fields witness the achievements of sequence-to-sequence models (e. g., LSTM+attention, Pointer Generator Networks, and Transformer) to enhance dialogue content generation.

Dialogue Generation

AI-lead Court Debate Case Investigation

no code implementations22 Oct 2020 Changzhen Ji, Xin Zhou, Conghui Zhu, Tiejun Zhao

The multi-role judicial debate composed of the plaintiff, defendant, and judge is an important part of the judicial trial.

Question Generation Text Generation

Reliable Evaluations for Natural Language Inference based on a Unified Cross-dataset Benchmark

no code implementations15 Oct 2020 Guanhua Zhang, Bing Bai, Jian Liang, Kun Bai, Conghui Zhu, Tiejun Zhao

Recent studies show that crowd-sourced Natural Language Inference (NLI) datasets may suffer from significant biases like annotation artifacts.

Natural Language Inference

End-to-End Speech Translation with Adversarial Training

no code implementations WS 2020 Xuancai Li, Chen Kehai, Tiejun Zhao, Muyun Yang

End-to-End speech translation usually leverages audio-to-text parallel data to train an available speech translation model which has shown impressive results on various speech translation tasks.

Translation

Demographics Should Not Be the Reason of Toxicity: Mitigating Discrimination in Text Classifications with Instance Weighting

1 code implementation ACL 2020 Guanhua Zhang, Bing Bai, Junqi Zhang, Kun Bai, Conghui Zhu, Tiejun Zhao

In this paper, we formalize the unintended biases in text classification datasets as a kind of selection bias from the non-discrimination distribution to the discrimination distribution.

Abusive Language General Classification +2

Self-Training for Unsupervised Neural Machine Translation in Unbalanced Training Data Scenarios

no code implementations NAACL 2021 Haipeng Sun, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao

Unsupervised neural machine translation (UNMT) that relies solely on massive monolingual corpora has achieved remarkable results in several translation tasks.

Machine Translation Translation

Understanding Learning Dynamics for Neural Machine Translation

no code implementations5 Apr 2020 Conghui Zhu, Guanlin Li, Lemao Liu, Tiejun Zhao, Shuming Shi

Despite the great success of NMT, there still remains a severe challenge: it is hard to interpret the internal dynamics during its training process.

Machine Translation Translation

Look-into-Object: Self-supervised Structure Modeling for Object Recognition

2 code implementations CVPR 2020 Mohan Zhou, Yalong Bai, Wei zhang, Tiejun Zhao, Tao Mei

Specifically, we first propose an object-extent learning module for localizing the object according to the visual patterns shared among the instances in the same category.

Fine-Grained Image Classification Image Recognition +5

Modeling Future Cost for Neural Machine Translation

no code implementations28 Feb 2020 Chaoqun Duan, Kehai Chen, Rui Wang, Masao Utiyama, Eiichiro Sumita, Conghui Zhu, Tiejun Zhao

Existing neural machine translation (NMT) systems utilize sequence-to-sequence neural networks to generate target translation word by word, and then make the generated word at each time-step and the counterpart in the references as consistent as possible.

Machine Translation Translation

Multimodal Matching Transformer for Live Commenting

no code implementations7 Feb 2020 Chaoqun Duan, Lei Cui, Shuming Ma, Furu Wei, Conghui Zhu, Tiejun Zhao

In this work, we aim to improve the relevance between live comments and videos by modeling the cross-modal interactions among different modalities.

Text Generation

Duality Regularization for Unsupervised Bilingual Lexicon Induction

no code implementations3 Sep 2019 Xuefeng Bai, Yue Zhang, Hailong Cao, Tiejun Zhao

Unsupervised bilingual lexicon induction naturally exhibits duality, which results from symmetry in back-translation.

Bilingual Lexicon Induction Translation

Revisiting Simple Domain Adaptation Methods in Unsupervised Neural Machine Translation

no code implementations26 Aug 2019 Haipeng Sun, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao, Chenhui Chu

However, it has not been well-studied for unsupervised neural machine translation (UNMT), although UNMT has recently achieved remarkable results in several domain-specific language pairs.

Domain Adaptation Machine Translation +1

Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation

no code implementations ACL 2019 Haipeng Sun, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao

In previous methods, UBWE is first trained using non-parallel monolingual corpora and then this pre-trained UBWE is used to initialize the word embedding in the encoder and decoder of UNMT.

Denoising Machine Translation +1

Sentence-Level Agreement for Neural Machine Translation

no code implementations ACL 2019 Mingming Yang, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Min Zhang, Tiejun Zhao

The training objective of neural machine translation (NMT) is to minimize the loss between the words in the translated sentences and those in the references.

Machine Translation Translation

Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets

2 code implementations ACL 2019 Guanhua Zhang, Bing Bai, Jian Liang, Kun Bai, Shiyu Chang, Mo Yu, Conghui Zhu, Tiejun Zhao

Natural Language Sentence Matching (NLSM) has gained substantial attention from both academics and the industry, and rich public datasets contribute a lot to this process.

Selection bias

Deep Attention Neural Tensor Network for Visual Question Answering

no code implementations ECCV 2018 Yalong Bai, Jianlong Fu, Tiejun Zhao, Tao Mei

First, we model one of the pairwise interaction (e. g., image and question) by bilinear features, which is further encoded with the third dimension (e. g., answer) to be a triplet by bilinear tensor product.

Deep Attention Question Answering +1

Forest-Based Neural Machine Translation

no code implementations ACL 2018 Chunpeng Ma, Akihiro Tamura, Masao Utiyama, Tiejun Zhao, Eiichiro Sumita

Tree-based neural machine translation (NMT) approaches, although achieved impressive performance, suffer from a major drawback: they only use the 1-best parse tree to direct the translation, which potentially introduces translation mistakes due to parsing errors.

Machine Translation Translation

Table-to-Text: Describing Table Region with Natural Language

no code implementations29 May 2018 Junwei Bao, Duyu Tang, Nan Duan, Zhao Yan, Yuanhua Lv, Ming Zhou, Tiejun Zhao

The model maps a row from a table to a continuous vector and then generates a natural language sentence by leveraging the semantics of a table.

Language Modelling

Syntax-Directed Attention for Neural Machine Translation

no code implementations12 Nov 2017 Kehai Chen, Rui Wang, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao

In this paper, we extend local attention with syntax-distance constraint, to focus on syntactically related source words with the predicted target word, thus learning a more effective context vector for word prediction.

Machine Translation Translation

Context-Aware Smoothing for Neural Machine Translation

no code implementations IJCNLP 2017 Kehai Chen, Rui Wang, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao

In Neural Machine Translation (NMT), each word is represented as a low-dimension, real-value vector for encoding its syntax and semantic information.

Machine Translation Representation Learning +1

Automatic Dataset Augmentation

no code implementations28 Aug 2017 Yalong Bai, Kuiyuan Yang, Tao Mei, Wei-Ying Ma, Tiejun Zhao

Large scale image dataset and deep convolutional neural network (DCNN) are two primary driving forces for the rapid progress made in generic object recognition tasks in recent years.

Object Recognition

Constraint-Based Question Answering with Knowledge Graph

1 code implementation COLING 2016 Junwei Bao, Nan Duan, Zhao Yan, Ming Zhou, Tiejun Zhao

WebQuestions and SimpleQuestions are two benchmark data-sets commonly used in recent knowledge-based question answering (KBQA) work.

Question Answering

Building A Case-based Semantic English-Chinese Parallel Treebank

no code implementations LREC 2016 Huaxing Shi, Tiejun Zhao, Keh-Yih Su

This Treebank is a part of a semantic corpus building project, which aims to build a semantic bilingual corpus annotated with syntactic, semantic cases and word senses.

Machine Translation POS +1

Augmenting Phrase Table by Employing Lexicons for Pivot-based SMT

no code implementations1 Dec 2015 Yiming Cui, Conghui Zhu, Xiaoning Zhu, Tiejun Zhao

Pivot language is employed as a way to solve the data sparseness problem in machine translation, especially when the data for a particular language pair does not exist.

Machine Translation Translation

Learning High-level Image Representation for Image Retrieval via Multi-Task DNN using Clickthrough Data

no code implementations17 Dec 2013 Yalong Bai, Kuiyuan Yang, Wei Yu, Wei-Ying Ma, Tiejun Zhao

Image retrieval refers to finding relevant images from an image database for a query, which is considered difficult for the gap between low-level representation of images and high-level representation of queries.

Image Retrieval

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