1 code implementation • 23 Dec 2024 • Jiaan Wang, Fandong Meng, Yunlong Liang, Jie zhou
Using Qwen2. 5 and LLama-3. 1 as the backbones, DRT-o1 models can learn the thought process during machine translation, and outperform vanilla LLMs as well as existing O1-like LLMs, showing their effectiveness The project is available at https://github. com/krystalan/DRT-o1
1 code implementation • 24 Jun 2024 • Xue Zhang, Yunlong Liang, Fandong Meng, Songming Zhang, Yufeng Chen, Jinan Xu, Jie zhou
To address this issue, we first investigate how LLMs process multilingual factual knowledge and discover that the same factual knowledge in different languages generally activates a shared set of neurons, which we call language-agnostic factual neurons (LAFNs).
no code implementations • 12 Dec 2023 • Xiangyu Shi, Yunlong Liang, Jinan Xu, Yufeng Chen
The results show that our method succeeds in reducing redundant retrieval operations and significantly reduces the overhead of kNN retrievals by up to 53% at the expense of a slight decline in translation quality.
1 code implementation • 20 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.
4 code implementations • 16 Sep 2023 • Jiaan Wang, Yunlong Liang, Zengkui Sun, Yuxuan Cao, Jiarong Xu, Fandong Meng
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.
no code implementations • 17 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.
1 code implementation • 22 May 2023 • Yunlong Liang, Fandong Meng, Jiaan Wang, Jinan Xu, Yufeng Chen, Jie zhou
Further, we propose a dual knowledge distillation and target-oriented vision modeling framework for the M$^3$S task.
no code implementations • 16 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.
1 code implementation • 14 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.
no code implementations • 13 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.
no code implementations • 4 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.
1 code implementation • 7 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.
no code implementations • 28 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.
no code implementations • 27 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.
1 code implementation • 15 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.
no code implementations • 14 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.
no code implementations • 28 Nov 2022 • Yunlong Liang, Fandong Meng, Jinan Xu, Yufeng Chen, Jie zhou
Our systems achieve 0. 810 and 0. 946 COMET scores.
1 code implementation • ACL 2022 • Yunlong Liang, Fandong Meng, Jinan Xu, Yufeng Chen, Jie zhou
Neural Chat Translation (NCT) aims to translate conversational text into different languages.
no code implementations • 23 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).
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).
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.
1 code implementation • EMNLP 2021 • Yunlong Liang, Chulun Zhou, Fandong Meng, Jinan Xu, Yufeng Chen, Jinsong Su, Jie zhou
Neural Chat Translation (NCT) aims to translate conversational text between speakers of different languages.
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.
1 code implementation • 9 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.
no code implementations • 12 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
3 code implementations • 4 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
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
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