Yufei Wang


2022

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PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks
Yufei Wang | Can Xu | Qingfeng Sun | Huang Hu | Chongyang Tao | Xiubo Geng | Daxin Jiang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

This paper focuses on the Data Augmentation for low-resource Natural Language Understanding (NLU) tasks. We propose Prompt-based Data Augmentation model (PromDA) which only trains small-scale Soft Prompt (i.e., a set of trainable vectors) in the frozen Pre-trained Language Models (PLMs). This avoids human effort in collecting unlabeled in-domain data and maintains the quality of generated synthetic data. In addition, PromDA generates synthetic data via two different views and filters out the low-quality data using NLU models. Experiments on four benchmarks show that synthetic data produced by PromDA successfully boost up the performance of NLU models which consistently outperform several competitive baseline models, including a state-of-the-art semi-supervised model using unlabeled in-domain data. The synthetic data from PromDA are also complementary with unlabeled in-domain data. The NLU models can be further improved when they are combined for training.

2021

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ECOL-R: Encouraging Copying in Novel Object Captioning with Reinforcement Learning
Yufei Wang | Ian Wood | Stephen Wan | Mark Johnson
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Novel Object Captioning is a zero-shot Image Captioning task requiring describing objects not seen in the training captions, but for which information is available from external object detectors. The key challenge is to select and describe all salient detected novel objects in the input images. In this paper, we focus on this challenge and propose the ECOL-R model (Encouraging Copying of Object Labels with Reinforced Learning), a copy-augmented transformer model that is encouraged to accurately describe the novel object labels. This is achieved via a specialised reward function in the SCST reinforcement learning framework (Rennie et al., 2017) that encourages novel object mentions while maintaining the caption quality. We further restrict the SCST training to the images where detected objects are mentioned in reference captions to train the ECOL-R model. We additionally improve our copy mechanism via Abstract Labels, which transfer knowledge from known to novel object types, and a Morphological Selector, which determines the appropriate inflected forms of novel object labels. The resulting model sets new state-of-the-art on the nocaps (Agrawal et al., 2019) and held-out COCO (Hendricks et al., 2016) benchmarks.

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Mention Flags (MF): Constraining Transformer-based Text Generators
Yufei Wang | Ian Wood | Stephen Wan | Mark Dras | Mark Johnson
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

This paper focuses on Seq2Seq (S2S) constrained text generation where the text generator is constrained to mention specific words which are inputs to the encoder in the generated outputs. Pre-trained S2S models or a Copy Mechanism are trained to copy the surface tokens from encoders to decoders, but they cannot guarantee constraint satisfaction. Constrained decoding algorithms always produce hypotheses satisfying all constraints. However, they are computationally expensive and can lower the generated text quality. In this paper, we propose Mention Flags (MF), which traces whether lexical constraints are satisfied in the generated outputs in an S2S decoder. The MF models can be trained to generate tokens in a hypothesis until all constraints are satisfied, guaranteeing high constraint satisfaction. Our experiments on the Common Sense Generation task (CommonGen) (Lin et al., 2020), End2end Restaurant Dialog task (E2ENLG) (Duˇsek et al., 2020) and Novel Object Captioning task (nocaps) (Agrawal et al., 2019) show that the MF models maintain higher constraint satisfaction and text quality than the baseline models and other constrained decoding algorithms, achieving state-of-the-art performance on all three tasks. These results are achieved with a much lower run-time than constrained decoding algorithms. We also show that the MF models work well in the low-resource setting.

2019

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How to Best Use Syntax in Semantic Role Labelling
Yufei Wang | Mark Johnson | Stephen Wan | Yifang Sun | Wei Wang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

There are many different ways in which external information might be used in a NLP task. This paper investigates how external syntactic information can be used most effectively in the Semantic Role Labeling (SRL) task. We evaluate three different ways of encoding syntactic parses and three different ways of injecting them into a state-of-the-art neural ELMo-based SRL sequence labelling model. We show that using a constituency representation as input features improves performance the most, achieving a new state-of-the-art for non-ensemble SRL models on the in-domain CoNLL’05 and CoNLL’12 benchmarks.

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Neural Constituency Parsing of Speech Transcripts
Paria Jamshid Lou | Yufei Wang | Mark Johnson
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

This paper studies the performance of a neural self-attentive parser on transcribed speech. Speech presents parsing challenges that do not appear in written text, such as the lack of punctuation and the presence of speech disfluencies (including filled pauses, repetitions, corrections, etc.). Disfluencies are especially problematic for conventional syntactic parsers, which typically fail to find any EDITED disfluency nodes at all. This motivated the development of special disfluency detection systems, and special mechanisms added to parsers specifically to handle disfluencies. However, we show here that neural parsers can find EDITED disfluency nodes, and the best neural parsers find them with an accuracy surpassing that of specialized disfluency detection systems, thus making these specialized mechanisms unnecessary. This paper also investigates a modified loss function that puts more weight on EDITED nodes. It also describes tree-transformations that simplify the disfluency detection task by providing alternative encodings of disfluencies and syntactic information.

2017

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SuperOCR for ALTA 2017 Shared Task
Yufei Wang
Proceedings of the Australasian Language Technology Association Workshop 2017

2016

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Data61-CSIRO systems at the CLPsych 2016 Shared Task
Sunghwan Mac Kim | Yufei Wang | Stephen Wan | Cécile Paris
Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology

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The Role of Features and Context on Suicide Ideation Detection
Yufei Wang | Stephen Wan | Cécile Paris
Proceedings of the Australasian Language Technology Association Workshop 2016