Yu Xu


2023

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ConFEDE: Contrastive Feature Decomposition for Multimodal Sentiment Analysis
Jiuding Yang | Yakun Yu | Di Niu | Weidong Guo | Yu Xu
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Multimodal Sentiment Analysis aims to predict the sentiment of video content. Recent research suggests that multimodal sentiment analysis critically depends on learning a good representation of multimodal information, which should contain both modality-invariant representations that are consistent across modalities as well as modality-specific representations. In this paper, we propose ConFEDE, a unified learning framework that jointly performs contrastive representation learning and contrastive feature decomposition to enhance the representation of multimodal information. It decomposes each of the three modalities of a video sample, including text, video frames, and audio, into a similarity feature and a dissimilarity feature, which are learned by a contrastive relation centered around the text. We conducted extensive experiments on CH-SIMS, MOSI and MOSEI to evaluate various state-of-the-art multimodal sentiment analysis methods. Experimental results show that ConFEDE outperforms all baselines on these datasets on a range of metrics.

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Exploiting Hierarchically Structured Categories in Fine-grained Chinese Named Entity Recognition
Jiuding Yang | Jinwen Luo | Weidong Guo | Di Niu | Yu Xu
Findings of the Association for Computational Linguistics: ACL 2023

Chinese Named Entity Recognition (CNER) is a widely used technology in various applications. While recent studies have focused on utilizing additional information of the Chinese language and characters to enhance CNER performance, this paper focuses on a specific aspect of CNER known as fine-grained CNER (FG-CNER). FG-CNER involves the use of hierarchical, fine-grained categories (e.g. Person-MovieStar) to label named entities. To promote research in this area, we introduce the FiNE dataset, a dataset for FG-CNER consisting of 30,000 sentences from various domains and containing 67,651 entities in 54 fine-grained flattened hierarchical categories. Additionally, we propose SoftFiNE, a novel approach for FG-CNER that utilizes a custom-designed relevance scoring function based on label structures to learn the potential relevance between different flattened hierarchical labels. Our experimental results demonstrate that the proposed SoftFiNE method outperforms the state-of-the-art baselines on the FiNE dataset. Furthermore, we conduct extensive experiments on three other datasets, including OntoNotes 4.0, Weibo, and Resume, where SoftFiNE achieved state-of-the-art performance on all three datasets.

2022

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MatRank: Text Re-ranking by Latent Preference Matrix
Jinwen Luo | Jiuding Yang | Weidong Guo | Chenglin Li | Di Niu | Yu Xu
Findings of the Association for Computational Linguistics: EMNLP 2022

Text ranking plays a key role in providing content that best answers user queries. It is usually divided into two sub-tasks to perform efficient information retrieval given a query: text retrieval and text re-ranking. Recent research on pretrained language models (PLM) has demonstrated efficiency and gain on both sub-tasks. However, while existing methods have benefited from pre-trained language models and achieved high recall rates on passage retrieval, the ranking performance still demands further improvement. In this paper, we propose MatRank, which learns to re-rank the text retrieved for a given query by learning to predict the most relevant passage based on a latent preference matrix. Specifically, MatRank uses a PLM to generate an asymmetric latent matrix of relative preference scores between all pairs of retrieved passages. Then, the latent matrix is aggregated row-wise and column-wise to obtain global preferences and predictions of the most relevant passage in two of these directions, respectively. We conduct extensive experiments on MS MACRO, WikiAQ, and SemEval datasets. Experimental results show that MatRank has achieved new state-of-the-art results on these datasets, outperforming all prior methods on ranking performance metrics.

2019

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Matching Article Pairs with Graphical Decomposition and Convolutions
Bang Liu | Di Niu | Haojie Wei | Jinghong Lin | Yancheng He | Kunfeng Lai | Yu Xu
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Identifying the relationship between two articles, e.g., whether two articles published from different sources describe the same breaking news, is critical to many document understanding tasks. Existing approaches for modeling and matching sentence pairs do not perform well in matching longer documents, which embody more complex interactions between the enclosed entities than a sentence does. To model article pairs, we propose the Concept Interaction Graph to represent an article as a graph of concepts. We then match a pair of articles by comparing the sentences that enclose the same concept vertex through a series of encoding techniques, and aggregate the matching signals through a graph convolutional network. To facilitate the evaluation of long article matching, we have created two datasets, each consisting of about 30K pairs of breaking news articles covering diverse topics in the open domain. Extensive evaluations of the proposed methods on the two datasets demonstrate significant improvements over a wide range of state-of-the-art methods for natural language matching.

2013

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Leveraging Synthetic Discourse Data via Multi-task Learning for Implicit Discourse Relation Recognition
Man Lan | Yu Xu | Zhengyu Niu
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2010

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ECNU: Effective Semantic Relations Classification without Complicated Features or Multiple External Corpora
Yuan Chen | Man Lan | Jian Su | Zhi Min Zhou | Yu Xu
Proceedings of the 5th International Workshop on Semantic Evaluation

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The Effects of Discourse Connectives Prediction on Implicit Discourse Relation Recognition
Zhi Min Zhou | Man Lan | Zheng Yu Niu | Yu Xu | Jian Su
Proceedings of the SIGDIAL 2010 Conference

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Predicting Discourse Connectives for Implicit Discourse Relation Recognition
Zhi-Min Zhou | Yu Xu | Zheng-Yu Niu | Man Lan | Jian Su | Chew Lim Tan
Coling 2010: Posters