Danlu Chen


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VizSeq: a visual analysis toolkit for text generation tasks
Changhan Wang | Anirudh Jain | Danlu Chen | Jiatao Gu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

Automatic evaluation of text generation tasks (e.g. machine translation, text summarization, image captioning and video description) usually relies heavily on task-specific metrics, such as BLEU and ROUGE. They, however, are abstract numbers and are not perfectly aligned with human assessment. This suggests inspecting detailed examples as a complement to identify system error patterns. In this paper, we present VizSeq, a visual analysis toolkit for instance-level and corpus-level system evaluation on a wide variety of text generation tasks. It supports multimodal sources and multiple text references, providing visualization in Jupyter notebook or a web app interface. It can be used locally or deployed onto public servers for centralized data hosting and benchmarking. It covers most common n-gram based metrics accelerated with multiprocessing, and also provides latest embedding-based metrics such as BERTScore.


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A Simple yet Effective Joint Training Method for Cross-Lingual Universal Dependency Parsing
Danlu Chen | Mengxiao Lin | Zhifeng Hu | Xipeng Qiu
Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

This paper describes Fudan’s submission to CoNLL 2018’s shared task Universal Dependency Parsing. We jointly train models when two languages are similar according to linguistic typology and then ensemble the models using a simple re-parse algorithm. We outperform the baseline method by 4.4% (2.1%) on average on development (test) set in CoNLL 2018 UD Shared Task.


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Cached Long Short-Term Memory Neural Networks for Document-Level Sentiment Classification
Jiacheng Xu | Danlu Chen | Xipeng Qiu | Xuanjing Huang
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing