Search Results for author: Yunlong Liang

Found 26 papers, 15 papers with code

Towards Faster k-Nearest-Neighbor Machine Translation

no code implementations12 Dec 2023 Xiangyu Shi, Yunlong Liang, Jinan Xu, Yufeng Chen

Recent works have proven the effectiveness of k-nearest-neighbor machine translation(a. k. a kNN-MT) approaches to produce remarkable improvement in cross-domain translations.

Machine Translation Retrieval +1

A Quality-based Syntactic Template Retriever for Syntactically-controlled Paraphrase Generation

1 code implementation20 Oct 2023 Xue Zhang, Songming Zhang, Yunlong Liang, Yufeng Chen, Jian Liu, Wenjuan Han, Jinan Xu

Furthermore, for situations requiring multiple paraphrases for each source sentence, we design a Diverse Templates Search (DTS) algorithm, which can enhance the diversity between paraphrases without sacrificing quality.

Data Augmentation Paraphrase Generation +2

Cross-Lingual Knowledge Editing in Large Language Models

2 code implementations16 Sep 2023 Jiaan Wang, Yunlong Liang, Zengkui Sun, Yuxuan Cao, Jiarong Xu

With the recent advancements in large language models (LLMs), knowledge editing has been shown as a promising technique to adapt LLMs to new knowledge without retraining from scratch.

knowledge editing

Snowman: A Million-scale Chinese Commonsense Knowledge Graph Distilled from Foundation Model

no code implementations17 Jun 2023 Jiaan Wang, Jianfeng Qu, Yunlong Liang, Zhixu Li, An Liu, Guanfeng Liu, Xin Zheng

Constructing commonsense knowledge graphs (CKGs) has attracted wide research attention due to its significant importance in cognitive intelligence.

Knowledge Graphs

Towards Unifying Multi-Lingual and Cross-Lingual Summarization

no code implementations16 May 2023 Jiaan Wang, Fandong Meng, Duo Zheng, Yunlong Liang, Zhixu Li, Jianfeng Qu, Jie zhou

In this paper, we aim to unify MLS and CLS into a more general setting, i. e., many-to-many summarization (M2MS), where a single model could process documents in any language and generate their summaries also in any language.

Language Modelling Text Summarization

Towards Understanding and Improving Knowledge Distillation for Neural Machine Translation

1 code implementation14 May 2023 Songming Zhang, Yunlong Liang, Shuaibo Wang, Wenjuan Han, Jian Liu, Jinan Xu, Yufeng Chen

In this work, we first unravel this mystery from an empirical perspective and show that the knowledge comes from the top-1 predictions of teachers, which also helps us build a potential connection between word- and sequence-level KD.

Knowledge Distillation Machine Translation +2

RC3: Regularized Contrastive Cross-lingual Cross-modal Pre-training

no code implementations13 May 2023 Chulun Zhou, Yunlong Liang, Fandong Meng, Jinan Xu, Jinsong Su, Jie zhou

In this paper, we propose Regularized Contrastive Cross-lingual Cross-modal (RC^3) pre-training, which further exploits more abundant weakly-aligned multilingual image-text pairs.

Contrastive Learning Machine Translation

Unified Model Learning for Various Neural Machine Translation

no code implementations4 May 2023 Yunlong Liang, Fandong Meng, Jinan Xu, Jiaan Wang, Yufeng Chen, Jie zhou

Specifically, we propose a ``versatile'' model, i. e., the Unified Model Learning for NMT (UMLNMT) that works with data from different tasks, and can translate well in multiple settings simultaneously, and theoretically it can be as many as possible.

Document Translation Machine Translation +3

Is ChatGPT a Good NLG Evaluator? A Preliminary Study

1 code implementation7 Mar 2023 Jiaan Wang, Yunlong Liang, Fandong Meng, Zengkui Sun, Haoxiang Shi, Zhixu Li, Jinan Xu, Jianfeng Qu, Jie zhou

In detail, we regard ChatGPT as a human evaluator and give task-specific (e. g., summarization) and aspect-specific (e. g., relevance) instruction to prompt ChatGPT to evaluate the generated results of NLG models.

nlg evaluation Story Generation

Zero-Shot Cross-Lingual Summarization via Large Language Models

no code implementations28 Feb 2023 Jiaan Wang, Yunlong Liang, Fandong Meng, Beiqi Zou, Zhixu Li, Jianfeng Qu, Jie zhou

Given a document in a source language, cross-lingual summarization (CLS) aims to generate a summary in a different target language.

Informativeness

A Multi-task Multi-stage Transitional Training Framework for Neural Chat Translation

no code implementations27 Jan 2023 Chulun Zhou, Yunlong Liang, Fandong Meng, Jie zhou, Jinan Xu, Hongji Wang, Min Zhang, Jinsong Su

To address these issues, in this paper, we propose a multi-task multi-stage transitional (MMT) training framework, where an NCT model is trained using the bilingual chat translation dataset and additional monolingual dialogues.

NMT Sentence +1

Summary-Oriented Vision Modeling for Multimodal Abstractive Summarization

1 code implementation15 Dec 2022 Yunlong Liang, Fandong Meng, Jinan Xu, Jiaan Wang, Yufeng Chen, Jie zhou

However, less attention has been paid to the visual features from the perspective of the summary, which may limit the model performance, especially in the low- and zero-resource scenarios.

Abstractive Text Summarization

Understanding Translationese in Cross-Lingual Summarization

no code implementations14 Dec 2022 Jiaan Wang, Fandong Meng, Yunlong Liang, Tingyi Zhang, Jiarong Xu, Zhixu Li, Jie zhou

In detail, we find that (1) the translationese in documents or summaries of test sets might lead to the discrepancy between human judgment and automatic evaluation; (2) the translationese in training sets would harm model performance in real-world applications; (3) though machine-translated documents involve translationese, they are very useful for building CLS systems on low-resource languages under specific training strategies.

A Survey on Cross-Lingual Summarization

no code implementations23 Mar 2022 Jiaan Wang, Fandong Meng, Duo Zheng, Yunlong Liang, Zhixu Li, Jianfeng Qu, Jie zhou

Cross-lingual summarization is the task of generating a summary in one language (e. g., English) for the given document(s) in a different language (e. g., Chinese).

A Variational Hierarchical Model for Neural Cross-Lingual Summarization

1 code implementation ACL 2022 Yunlong Liang, Fandong Meng, Chulun Zhou, Jinan Xu, Yufeng Chen, Jinsong Su, Jie zhou

The goal of the cross-lingual summarization (CLS) is to convert a document in one language (e. g., English) to a summary in another one (e. g., Chinese).

Machine Translation Translation

MSCTD: A Multimodal Sentiment Chat Translation Dataset

1 code implementation ACL 2022 Yunlong Liang, Fandong Meng, Jinan Xu, Yufeng Chen, Jie zhou

In this work, we introduce a new task named Multimodal Chat Translation (MCT), aiming to generate more accurate translations with the help of the associated dialogue history and visual context.

Multimodal Machine Translation Sentiment Analysis +1

Modeling Bilingual Conversational Characteristics for Neural Chat Translation

1 code implementation ACL 2021 Yunlong Liang, Fandong Meng, Yufeng Chen, Jinan Xu, Jie zhou

Despite the impressive performance of sentence-level and context-aware Neural Machine Translation (NMT), there still remain challenges to translate bilingual conversational text due to its inherent characteristics such as role preference, dialogue coherence, and translation consistency.

Machine Translation NMT +2

Emotional Conversation Generation with Heterogeneous Graph Neural Network

1 code implementation9 Dec 2020 Yunlong Liang, Fandong Meng, Ying Zhang, Jinan Xu, Yufeng Chen, Jie zhou

Firstly, we design a Heterogeneous Graph-Based Encoder to represent the conversation content (i. e., the dialogue history, its emotion flow, facial expressions, audio, and speakers' personalities) with a heterogeneous graph neural network, and then predict suitable emotions for feedback.

Modeling Inter-Aspect Dependencies with a Non-temporal Mechanism for Aspect-Based Sentiment Analysis

no code implementations12 Aug 2020 Yunlong Liang, Fandong Meng, Jinchao Zhang, Yufeng Chen, Jinan Xu, Jie zhou

For multiple aspects scenario of aspect-based sentiment analysis (ABSA), existing approaches typically ignore inter-aspect relations or rely on temporal dependencies to process aspect-aware representations of all aspects in a sentence.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +1

An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment Analysis

2 code implementations Findings (EMNLP) 2021 Yunlong Liang, Fandong Meng, Jinchao Zhang, Yufeng Chen, Jinan Xu, Jie zhou

Aspect-based sentiment analysis (ABSA) mainly involves three subtasks: aspect term extraction, opinion term extraction, and aspect-level sentiment classification, which are typically handled in a separate or joint manner.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +3

A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment Analysis

3 code implementations4 Apr 2020 Yunlong Liang, Fandong Meng, Jinchao Zhang, Jinan Xu, Yufeng Chen, Jie zhou

The aspect-based sentiment analysis (ABSA) task remains to be a long-standing challenge, which aims to extract the aspect term and then identify its sentiment orientation. In previous approaches, the explicit syntactic structure of a sentence, which reflects the syntax properties of natural language and hence is intuitively crucial for aspect term extraction and sentiment recognition, is typically neglected or insufficiently modeled.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +3

A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis

1 code implementation IJCNLP 2019 Yunlong Liang, Fandong Meng, Jinchao Zhang, Jinan Xu, Yufeng Chen, Jie zhou

Aspect based sentiment analysis (ABSA) aims to identify the sentiment polarity towards the given aspect in a sentence, while previous models typically exploit an aspect-independent (weakly associative) encoder for sentence representation generation.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +1

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