Canwen Xu


2022

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BERT Learns to Teach: Knowledge Distillation with Meta Learning
Wangchunshu Zhou | Canwen Xu | Julian McAuley
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present Knowledge Distillation with Meta Learning (MetaDistil), a simple yet effective alternative to traditional knowledge distillation (KD) methods where the teacher model is fixed during training. We show the teacher network can learn to better transfer knowledge to the student network (i.e., learning to teach) with the feedback from the performance of the distilled student network in a meta learning framework. Moreover, we introduce a pilot update mechanism to improve the alignment between the inner-learner and meta-learner in meta learning algorithms that focus on an improved inner-learner. Experiments on various benchmarks show that MetaDistil can yield significant improvements compared with traditional KD algorithms and is less sensitive to the choice of different student capacity and hyperparameters, facilitating the use of KD on different tasks and models.

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PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts
Stephen Bach | Victor Sanh | Zheng Xin Yong | Albert Webson | Colin Raffel | Nihal V. Nayak | Abheesht Sharma | Taewoon Kim | M Saiful Bari | Thibault Fevry | Zaid Alyafeai | Manan Dey | Andrea Santilli | Zhiqing Sun | Srulik Ben-david | Canwen Xu | Gunjan Chhablani | Han Wang | Jason Fries | Maged Al-shaibani | Shanya Sharma | Urmish Thakker | Khalid Almubarak | Xiangru Tang | Dragomir Radev | Mike Tian-jian Jiang | Alexander Rush
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

PromptSource is a system for creating, sharing, and using natural language prompts. Prompts are functions that map an example from a dataset to a natural language input and target output. Using prompts to train and query language models is an emerging area in NLP that requires new tools that let users develop and refine these prompts collaboratively. PromptSource addresses the emergent challenges in this new setting with (1) a templating language for defining data-linked prompts, (2) an interface that lets users quickly iterate on prompt development by observing outputs of their prompts on many examples, and (3) a community-driven set of guidelines for contributing new prompts to a common pool. Over 2,000 prompts for roughly 170 datasets are already available in PromptSource. PromptSource is available at https://github.com/bigscience-workshop/promptsource.

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LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval
Canwen Xu | Daya Guo | Nan Duan | Julian McAuley
Findings of the Association for Computational Linguistics: ACL 2022

In this paper, we propose LaPraDoR, a pretrained dual-tower dense retriever that does not require any supervised data for training. Specifically, we first present Iterative Contrastive Learning (ICoL) that iteratively trains the query and document encoders with a cache mechanism. ICoL not only enlarges the number of negative instances but also keeps representations of cached examples in the same hidden space. We then propose Lexicon-Enhanced Dense Retrieval (LEDR) as a simple yet effective way to enhance dense retrieval with lexical matching. We evaluate LaPraDoR on the recently proposed BEIR benchmark, including 18 datasets of 9 zero-shot text retrieval tasks. Experimental results show that LaPraDoR achieves state-of-the-art performance compared with supervised dense retrieval models, and further analysis reveals the effectiveness of our training strategy and objectives. Compared to re-ranking, our lexicon-enhanced approach can be run in milliseconds (22.5x faster) while achieving superior performance.

2021

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Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting
Wangchunshu Zhou | Tao Ge | Canwen Xu | Ke Xu | Furu Wei
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

In this paper, we propose Sequence Span Rewriting (SSR), a self-supervised task for sequence-to-sequence (Seq2Seq) pre-training. SSR learns to refine the machine-generated imperfect text spans into ground truth text. SSR provides more fine-grained and informative supervision in addition to the original text-infilling objective. Compared to the prevalent text infilling objectives for Seq2Seq pre-training, SSR is naturally more consistent with many downstream generation tasks that require sentence rewriting (e.g., text summarization, question generation, grammatical error correction, and paraphrase generation). We conduct extensive experiments by using SSR to improve the typical Seq2Seq pre-trained model T5 in a continual pre-training setting and show substantial improvements over T5 on various natural language generation tasks.

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Beyond Preserved Accuracy: Evaluating Loyalty and Robustness of BERT Compression
Canwen Xu | Wangchunshu Zhou | Tao Ge | Ke Xu | Julian McAuley | Furu Wei
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Recent studies on compression of pretrained language models (e.g., BERT) usually use preserved accuracy as the metric for evaluation. In this paper, we propose two new metrics, label loyalty and probability loyalty that measure how closely a compressed model (i.e., student) mimics the original model (i.e., teacher). We also explore the effect of compression with regard to robustness under adversarial attacks. We benchmark quantization, pruning, knowledge distillation and progressive module replacing with loyalty and robustness. By combining multiple compression techniques, we provide a practical strategy to achieve better accuracy, loyalty and robustness.

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Datasets: A Community Library for Natural Language Processing
Quentin Lhoest | Albert Villanova del Moral | Yacine Jernite | Abhishek Thakur | Patrick von Platen | Suraj Patil | Julien Chaumond | Mariama Drame | Julien Plu | Lewis Tunstall | Joe Davison | Mario Šaško | Gunjan Chhablani | Bhavitvya Malik | Simon Brandeis | Teven Le Scao | Victor Sanh | Canwen Xu | Nicolas Patry | Angelina McMillan-Major | Philipp Schmid | Sylvain Gugger | Clément Delangue | Théo Matussière | Lysandre Debut | Stas Bekman | Pierric Cistac | Thibault Goehringer | Victor Mustar | François Lagunas | Alexander Rush | Thomas Wolf
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

The scale, variety, and quantity of publicly-available NLP datasets has grown rapidly as researchers propose new tasks, larger models, and novel benchmarks. Datasets is a community library for contemporary NLP designed to support this ecosystem. Datasets aims to standardize end-user interfaces, versioning, and documentation, while providing a lightweight front-end that behaves similarly for small datasets as for internet-scale corpora. The design of the library incorporates a distributed, community-driven approach to adding datasets and documenting usage. After a year of development, the library now includes more than 650 unique datasets, has more than 250 contributors, and has helped support a variety of novel cross-dataset research projects and shared tasks. The library is available at https://github.com/huggingface/datasets.

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Blow the Dog Whistle: A Chinese Dataset for Cant Understanding with Common Sense and World Knowledge
Canwen Xu | Wangchunshu Zhou | Tao Ge | Ke Xu | Julian McAuley | Furu Wei
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Cant is important for understanding advertising, comedies and dog-whistle politics. However, computational research on cant is hindered by a lack of available datasets. In this paper, we propose a large and diverse Chinese dataset for creating and understanding cant from a computational linguistics perspective. We formulate a task for cant understanding and provide both quantitative and qualitative analysis for tested word embedding similarity and pretrained language models. Experiments suggest that such a task requires deep language understanding, common sense, and world knowledge and thus can be a good testbed for pretrained language models and help models perform better on other tasks.

2020

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Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-Encoders
Yu Duan | Canwen Xu | Jiaxin Pei | Jialong Han | Chenliang Li
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Conditional Text Generation has drawn much attention as a topic of Natural Language Generation (NLG) which provides the possibility for humans to control the properties of generated contents. Current conditional generation models cannot handle emerging conditions due to their joint end-to-end learning fashion. When a new condition added, these techniques require full retraining. In this paper, we present a new framework named Pre-train and Plug-in Variational Auto-Encoder (PPVAE) towards flexible conditional text generation. PPVAE decouples the text generation module from the condition representation module to allow “one-to-many” conditional generation. When a fresh condition emerges, only a lightweight network needs to be trained and works as a plug-in for PPVAE, which is efficient and desirable for real-world applications. Extensive experiments demonstrate the superiority of PPVAE against the existing alternatives with better conditionality and diversity but less training effort.

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MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization
Canwen Xu | Jiaxin Pei | Hongtao Wu | Yiyu Liu | Chenliang Li
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Recently, large-scale datasets have vastly facilitated the development in nearly all domains of Natural Language Processing. However, there is currently no cross-task dataset in NLP, which hinders the development of multi-task learning. We propose MATINF, the first jointly labeled large-scale dataset for classification, question answering and summarization. MATINF contains 1.07 million question-answer pairs with human-labeled categories and user-generated question descriptions. Based on such rich information, MATINF is applicable for three major NLP tasks, including classification, question answering, and summarization. We benchmark existing methods and a novel multi-task baseline over MATINF to inspire further research. Our comprehensive comparison and experiments over MATINF and other datasets demonstrate the merits held by MATINF.

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BERT-of-Theseus: Compressing BERT by Progressive Module Replacing
Canwen Xu | Wangchunshu Zhou | Tao Ge | Furu Wei | Ming Zhou
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

In this paper, we propose a novel model compression approach to effectively compress BERT by progressive module replacing. Our approach first divides the original BERT into several modules and builds their compact substitutes. Then, we randomly replace the original modules with their substitutes to train the compact modules to mimic the behavior of the original modules. We progressively increase the probability of replacement through the training. In this way, our approach brings a deeper level of interaction between the original and compact models. Compared to the previous knowledge distillation approaches for BERT compression, our approach does not introduce any additional loss function. Our approach outperforms existing knowledge distillation approaches on GLUE benchmark, showing a new perspective of model compression.

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Transformers: State-of-the-Art Natural Language Processing
Thomas Wolf | Lysandre Debut | Victor Sanh | Julien Chaumond | Clement Delangue | Anthony Moi | Pierric Cistac | Tim Rault | Remi Louf | Morgan Funtowicz | Joe Davison | Sam Shleifer | Patrick von Platen | Clara Ma | Yacine Jernite | Julien Plu | Canwen Xu | Teven Le Scao | Sylvain Gugger | Mariama Drame | Quentin Lhoest | Alexander Rush
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Recent progress in natural language processing has been driven by advances in both model architecture and model pretraining. Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of tasks. Transformers is an open-source library with the goal of opening up these advances to the wider machine learning community. The library consists of carefully engineered state-of-the art Transformer architectures under a unified API. Backing this library is a curated collection of pretrained models made by and available for the community. Transformers is designed to be extensible by researchers, simple for practitioners, and fast and robust in industrial deployments. The library is available at https://github.com/huggingface/transformers.

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UnihanLM: Coarse-to-Fine Chinese-Japanese Language Model Pretraining with the Unihan Database
Canwen Xu | Tao Ge | Chenliang Li | Furu Wei
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

Chinese and Japanese share many characters with similar surface morphology. To better utilize the shared knowledge across the languages, we propose UnihanLM, a self-supervised Chinese-Japanese pretrained masked language model (MLM) with a novel two-stage coarse-to-fine training approach. We exploit Unihan, a ready-made database constructed by linguistic experts to first merge morphologically similar characters into clusters. The resulting clusters are used to replace the original characters in sentences for the coarse-grained pretraining of the MLM. Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters. We conduct extensive experiments on a variety of Chinese and Japanese NLP benchmarks, showing that our proposed UnihanLM is effective on both mono- and cross-lingual Chinese and Japanese tasks, shedding light on a new path to exploit the homology of languages.