Qiang Chen


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Commonsense Knowledge Salience Evaluation with a Benchmark Dataset in E-commerce
Yincen Qu | Ningyu Zhang | Hui Chen | Zelin Dai | Chengming Wang | Xiaoyu Wang | Qiang Chen | Huajun Chen
Findings of the Association for Computational Linguistics: EMNLP 2022

In e-commerce, the salience of commonsense knowledge (CSK) is beneficial for widespread applications such as product search and recommendation. For example, when users search for “running” in e-commerce, they would like to find products highly related to running, such as “running shoes” rather than “shoes”. Nevertheless, many existing CSK collections rank statements solely by confidence scores, and there is no information about which ones are salient from a human perspective. In this work, we define the task of supervised salience evaluation, where given a CSK triple, the model is required to learn whether the triple is salient or not. In addition to formulating the new task, we also release a new Benchmark dataset of Salience Evaluation in E-commerce (BSEE) and hope to promote related research on commonsense knowledge salience evaluation. We conduct experiments in the dataset with several representative baseline models. The experimental results show that salience evaluation is a hard task where models perform poorly on our evaluation set. We further propose a simple but effective approach, PMI-tuning, which shows promise for solving this novel problem. Code is available in https://github.com/OpenBGBenchmark/OpenBG-CSK.

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SelfMix: Robust Learning against Textual Label Noise with Self-Mixup Training
Dan Qiao | Chenchen Dai | Yuyang Ding | Juntao Li | Qiang Chen | Wenliang Chen | Min Zhang
Proceedings of the 29th International Conference on Computational Linguistics

The conventional success of textual classification relies on annotated data, and the new paradigm of pre-trained language models (PLMs) still requires a few labeled data for downstream tasks. However, in real-world applications, label noise inevitably exists in training data, damaging the effectiveness, robustness, and generalization of the models constructed on such data. Recently, remarkable achievements have been made to mitigate this dilemma in visual data, while only a few explore textual data. To fill this gap, we present SelfMix, a simple yet effective method, to handle label noise in text classification tasks. SelfMix uses the Gaussian Mixture Model to separate samples and leverages semi-supervised learning. Unlike previous works requiring multiple models, our method utilizes the dropout mechanism on a single model to reduce the confirmation bias in self-training and introduces a textual level mixup training strategy. Experimental results on three text classification benchmarks with different types of text show that the performance of our proposed method outperforms these strong baselines designed for both textual and visual data under different noise ratios and noise types. Our anonymous code is available at https://github.com/noise-learning/SelfMix.


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Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs
Mingyang Chen | Wen Zhang | Wei Zhang | Qiang Chen | Huajun Chen
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Link prediction is an important way to complete knowledge graphs (KGs), while embedding-based methods, effective for link prediction in KGs, perform poorly on relations that only have a few associative triples. In this work, we propose a Meta Relational Learning (MetaR) framework to do the common but challenging few-shot link prediction in KGs, namely predicting new triples about a relation by only observing a few associative triples. We solve few-shot link prediction by focusing on transferring relation-specific meta information to make model learn the most important knowledge and learn faster, corresponding to relation meta and gradient meta respectively in MetaR. Empirically, our model achieves state-of-the-art results on few-shot link prediction KG benchmarks.


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Learning to Adapt Credible Knowledge in Cross-lingual Sentiment Analysis
Qiang Chen | Wenjie Li | Yu Lei | Xule Liu | Yanxiang He
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)


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Deep Belief Networks and Biomedical Text Categorisation
Antonio Jimeno Yepes | Andrew MacKinlay | Justin Bedo | Rahil Garvani | Qiang Chen
Proceedings of the Australasian Language Technology Association Workshop 2014

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Identifying Twitter Location Mentions
Bo Han | Antonio Jimeno Yepes | Andrew MacKinlay | Qiang Chen
Proceedings of the Australasian Language Technology Association Workshop 2014