Junfeng Tian

Also published as: Jun Feng Tian


pdf bib
MinD at SemEval-2021 Task 6: Propaganda Detection using Transfer Learning and Multimodal Fusion
Junfeng Tian | Min Gui | Chenliang Li | Ming Yan | Wenming Xiao
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

We describe our systems of subtask1 and subtask3 for SemEval-2021 Task 6 on Detection of Persuasion Techniques in Texts and Images. The purpose of subtask1 is to identify propaganda techniques given textual content, and the goal of subtask3 is to detect them given both textual and visual content. For subtask1, we investigate transfer learning based on pre-trained language models (PLMs) such as BERT, RoBERTa to solve data sparsity problems. For subtask3, we extract heterogeneous visual representations (i.e., face features, OCR features, and multimodal representations) and explore various multimodal fusion strategies to combine the textual and visual representations. The official evaluation shows our ensemble model ranks 1st for subtask1 and 2nd for subtask3.


pdf bib
Multi-Domain Dialogue Acts and Response Co-Generation
Kai Wang | Junfeng Tian | Rui Wang | Xiaojun Quan | Jianxing Yu
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Generating fluent and informative responses is of critical importance for task-oriented dialogue systems. Existing pipeline approaches generally predict multiple dialogue acts first and use them to assist response generation. There are at least two shortcomings with such approaches. First, the inherent structures of multi-domain dialogue acts are neglected. Second, the semantic associations between acts and responses are not taken into account for response generation. To address these issues, we propose a neural co-generation model that generates dialogue acts and responses concurrently. Unlike those pipeline approaches, our act generation module preserves the semantic structures of multi-domain dialogue acts and our response generation module dynamically attends to different acts as needed. We train the two modules jointly using an uncertainty loss to adjust their task weights adaptively. Extensive experiments are conducted on the large-scale MultiWOZ dataset and the results show that our model achieves very favorable improvement over several state-of-the-art models in both automatic and human evaluations.

pdf bib
SentiX: A Sentiment-Aware Pre-Trained Model for Cross-Domain Sentiment Analysis
Jie Zhou | Junfeng Tian | Rui Wang | Yuanbin Wu | Wenming Xiao | Liang He
Proceedings of the 28th International Conference on Computational Linguistics

Pre-trained language models have been widely applied to cross-domain NLP tasks like sentiment analysis, achieving state-of-the-art performance. However, due to the variety of users’ emotional expressions across domains, fine-tuning the pre-trained models on the source domain tends to overfit, leading to inferior results on the target domain. In this paper, we pre-train a sentiment-aware language model (SentiX) via domain-invariant sentiment knowledge from large-scale review datasets, and utilize it for cross-domain sentiment analysis task without fine-tuning. We propose several pre-training tasks based on existing lexicons and annotations at both token and sentence levels, such as emoticons, sentiment words, and ratings, without human interference. A series of experiments are conducted and the results indicate the great advantages of our model. We obtain new state-of-the-art results in all the cross-domain sentiment analysis tasks, and our proposed SentiX can be trained with only 1% samples (18 samples) and it achieves better performance than BERT with 90% samples.


pdf bib
Attention Optimization for Abstractive Document Summarization
Min Gui | Junfeng Tian | Rui Wang | Zhenglu Yang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Attention plays a key role in the improvement of sequence-to-sequence-based document summarization models. To obtain a powerful attention helping with reproducing the most salient information and avoiding repetitions, we augment the vanilla attention model from both local and global aspects. We propose attention refinement unit paired with local variance loss to impose supervision on the attention model at each decoding step, and we also propose a global variance loss to optimize the attention distributions of all decoding steps from the global perspective. The performances on CNN/Daily Mail dataset verify the effectiveness of our methods.


pdf bib
ECNU at SemEval-2018 Task 12: An End-to-End Attention-based Neural Network for the Argument Reasoning Comprehension Task
Junfeng Tian | Man Lan | Yuanbin Wu
Proceedings of The 12th International Workshop on Semantic Evaluation

This paper presents our submissions to SemEval 2018 Task 12: the Argument Reasoning Comprehension Task. We investigate an end-to-end attention-based neural network to represent the two lexically close candidate warrants. On the one hand, we extract their different parts as attention vectors to obtain distinguishable representations. On the other hand, we use their surrounds (i.e., claim, reason, debate context) as another attention vectors to get contextual representations, which work as final clues to select the correct warrant. Our model achieves 60.4% accuracy and ranks 3rd among 22 participating systems.


pdf bib
ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity
Junfeng Tian | Zhiheng Zhou | Man Lan | Yuanbin Wu
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

To address semantic similarity on multilingual and cross-lingual sentences, we firstly translate other foreign languages into English, and then feed our monolingual English system with various interactive features. Our system is further supported by combining with deep learning semantic similarity and our best run achieves the mean Pearson correlation 73.16% in primary track.


pdf bib
ECNU at SemEval-2016 Task 1: Leveraging Word Embedding From Macro and Micro Views to Boost Performance for Semantic Textual Similarity
Junfeng Tian | Man Lan
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)


pdf bib
ECNU: Using Traditional Similarity Measurements and Word Embedding for Semantic Textual Similarity Estimation
Jiang Zhao | Man Lan | Jun Feng Tian
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)