Yi Li


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CLGC: A Corpus for Chinese Literary Grace Evaluation
Yi Li | Dong Yu | Pengyuan Liu
Proceedings of the Thirteenth Language Resources and Evaluation Conference

In this paper, we construct a Chinese literary grace corpus, CLGC, with 10,000 texts and more than 1.85 million tokens. Multi-level annotations are provided for each text in our corpus, including literary grace level, sentence category, and figure-of-speech type. Based on the corpus, we dig deep into the correlation between fine-grained features (semantic information, part-of-speech and figure-of-speech, etc.) and literary grace level. We also propose a new Literary Grace Evaluation (LGE) task, which aims at making a comprehensive assessment of the literary grace level according to the text. In the end, we build some classification models with machine learning algorithms (such as SVM, TextCNN) to prove the effectiveness of our features and corpus for LGE. The results of our preliminary classification experiments have achieved 79.71% on the weighted average F1-score.

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Multi-Attribute Controlled Text Generation with Contrastive-Generator and External-Discriminator
Guisheng Liu | Yi Li | Yanqing Guo | Xiangyang Luo | Bo Wang
Proceedings of the 29th International Conference on Computational Linguistics

Though existing researches have achieved impressive results in controlled text generation, they focus mainly on single-attribute control. However, in applications like automatic comments, the topic and sentiment need to be controlled simultaneously. In this work, we propose a new framework for multi-attribute controlled text generation. To achieve this, we design a contrastive-generator that can effectively generate texts with more attributes. In order to increase the convergence of the text on the desired attributes, we adopt an external-discriminator to distinguish whether the generated text holds the desired attributes. Moreover, we propose top-n weighted decoding to further improve the relevance of texts to attributes. Automated evaluations and human evaluations show that our framework achieves remarkable controllability in multi-attribute generation while keeping the text fluent and diverse. It also yields promising performance on zero-shot generation.


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Large Margin Neural Language Model
Jiaji Huang | Yi Li | Wei Ping | Liang Huang
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We propose a large margin criterion for training neural language models. Conventionally, neural language models are trained by minimizing perplexity (PPL) on grammatical sentences. However, we demonstrate that PPL may not be the best metric to optimize in some tasks, and further propose a large margin formulation. The proposed method aims to enlarge the margin between the “good” and “bad” sentences in a task-specific sense. It is trained end-to-end and can be widely applied to tasks that involve re-scoring of generated text. Compared with minimum-PPL training, our method gains up to 1.1 WER reduction for speech recognition and 1.0 BLEU increase for machine translation.


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Is This Post Persuasive? Ranking Argumentative Comments in Online Forum
Zhongyu Wei | Yang Liu | Yi Li
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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A Preliminary Study of Disputation Behavior in Online Debating Forum
Zhongyu Wei | Yandi Xia | Chen Li | Yang Liu | Zachary Stallbohm | Yi Li | Yang Jin
Proceedings of the Third Workshop on Argument Mining (ArgMining2016)


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Deploying MT into a Localisation Workflow: Pains and Gains
Yanli Sun | Juan Liu | Yi Li
Proceedings of Machine Translation Summit XIII: Papers


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Exploring Abbreviation Expansion for Genomic Information Retrieval
Nicola Stokes | Yi Li | Lawrence Cavedon | Justin Zobel
Proceedings of the Australasian Language Technology Workshop 2007