Jian Zhu


2023

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RWKV: Reinventing RNNs for the Transformer Era
Bo Peng | Eric Alcaide | Quentin Anthony | Alon Albalak | Samuel Arcadinho | Stella Biderman | Huanqi Cao | Xin Cheng | Michael Chung | Leon Derczynski | Xingjian Du | Matteo Grella | Kranthi Gv | Xuzheng He | Haowen Hou | Przemyslaw Kazienko | Jan Kocon | Jiaming Kong | Bartłomiej Koptyra | Hayden Lau | Jiaju Lin | Krishna Sri Ipsit Mantri | Ferdinand Mom | Atsushi Saito | Guangyu Song | Xiangru Tang | Johan Wind | Stanisław Woźniak | Zhenyuan Zhang | Qinghua Zhou | Jian Zhu | Rui-Jie Zhu
Findings of the Association for Computational Linguistics: EMNLP 2023

Transformers have revolutionized almost all natural language processing (NLP) tasks but suffer from memory and computational complexity that scales quadratically with sequence length. In contrast, recurrent neural networks (RNNs) exhibit linear scaling in memory and computational requirements but struggle to match the same performance as Transformers due to limitations in parallelization and scalability. We propose a novel model architecture, Receptance Weighted Key Value (RWKV), that combines the efficient parallelizable training of transformers with the efficient inference of RNNs. Our approach leverages a linear attention mechanism and allows us to formulate the model as either a Transformer or an RNN, thus parallelizing computations during training and maintains constant computational and memory complexity during inference. We scale our models as large as 14 billion parameters, by far the largest dense RNN ever trained, and find RWKV performs on par with similarly sized Transformers, suggesting future work can leverage this architecture to create more efficient models. This work presents a significant step towards reconciling trade-offs between computational efficiency and model performance in sequence processing tasks.

2022

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Bootstrapping meaning through listening: Unsupervised learning of spoken sentence embeddings
Jian Zhu | Zuoyu Tian | Yadong Liu | Cong Zhang | Chia-Wen Lo
Findings of the Association for Computational Linguistics: EMNLP 2022

Inducing semantic representations directly from speech signals is a highly challenging task but has many useful applications in speech mining and spoken language understanding. This study tackles the unsupervised learning of semantic representations for spoken utterances. Through converting speech signals into hidden units generated from acoustic unit discovery, we propose WavEmbed, a multimodal sequential autoencoder that predicts hidden units from a dense representation of speech. Secondly, we also propose S-HuBERT to induce meaning through knowledge distillation, in which a sentence embedding model is first trained on hidden units and passes its knowledge to a speech encoder through contrastive learning. The best performing model achieves a moderate correlation (0.5 0.6) with human judgments, without relying on any labels or transcriptions. Furthermore, these models can also be easily extended to leverage textual transcriptions of speech to learn much better speech embeddings that are strongly correlated with human annotations. Our proposed methods are applicable to the development of purely data-driven systems for speech mining, indexing and search.

2021

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Idiosyncratic but not Arbitrary: Learning Idiolects in Online Registers Reveals Distinctive yet Consistent Individual Styles
Jian Zhu | David Jurgens
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

An individual’s variation in writing style is often a function of both social and personal attributes. While structured social variation has been extensively studied, e.g., gender based variation, far less is known about how to characterize individual styles due to their idiosyncratic nature. We introduce a new approach to studying idiolects through a massive cross-author comparison to identify and encode stylistic features. The neural model achieves strong performance at authorship identification on short texts and through an analogy-based probing task, showing that the learned representations exhibit surprising regularities that encode qualitative and quantitative shifts of idiolectal styles. Through text perturbation, we quantify the relative contributions of different linguistic elements to idiolectal variation. Furthermore, we provide a description of idiolects through measuring inter- and intra-author variation, showing that variation in idiolects is often distinctive yet consistent.

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The structure of online social networks modulates the rate of lexical change
Jian Zhu | David Jurgens
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

New words are regularly introduced to communities, yet not all of these words persist in a community’s lexicon. Among the many factors contributing to lexical change, we focus on the understudied effect of social networks. We conduct a large-scale analysis of over 80k neologisms in 4420 online communities across a decade. Using Poisson regression and survival analysis, our study demonstrates that the community’s network structure plays a significant role in lexical change. Apart from overall size, properties including dense connections, the lack of local clusters, and more external contacts promote lexical innovation and retention. Unlike offline communities, these topic-based communities do not experience strong lexical leveling despite increased contact but accommodate more niche words. Our work provides support for the sociolinguistic hypothesis that lexical change is partially shaped by the structure of the underlying network but also uncovers findings specific to online communities.

2019

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UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs
Jian Zhu | Zuoyu Tian | Sandra Kübler
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper describes the UM-IU@LING’s system for the SemEval 2019 Task 6: Offens-Eval. We take a mixed approach to identify and categorize hate speech in social media. In subtask A, we fine-tuned a BERT based classifier to detect abusive content in tweets, achieving a macro F1 score of 0.8136 on the test data, thus reaching the 3rd rank out of 103 submissions. In subtasks B and C, we used a linear SVM with selected character n-gram features. For subtask C, our system could identify the target of abuse with a macro F1 score of 0.5243, ranking it 27th out of 65 submissions.

2011

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A New Unsupervised Approach to Word Segmentation
Hanshi Wang | Jian Zhu | Shiping Tang | Xiaozhong Fan
Computational Linguistics, Volume 37, Issue 3 - September 2011