Hongyu Guo


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Effectiveness of Data Augmentation for Parameter Efficient Tuning with Limited Data
Stephen Obadinma | Hongyu Guo | Xiaodan Zhu
Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023)

Recent work has demonstrated that using parameter efficient tuning techniques such as prefix tuning (or P-tuning) on pretrained language models can yield performance that is comparable or superior to fine-tuning while dramatically reducing trainable parameters. Nevertheless, the effectiveness of such methods under the context of data augmentation, a common strategy to improve learning under low data regimes, has not been fully explored. In this paper, we examine the effectiveness of several popular task-agnostic data augmentation techniques, i.e., EDA, Back Translation, and Mixup, when using two general parameter efficient tuning methods, P-tuning v2 and LoRA, under data scarcity. We show that data augmentation can be used to boost the performance of P-tuning and LoRA models, but the effectiveness of each technique varies and certain methods can lead to a notable degradation in performance, particularly when using larger models and on harder tasks. We further analyze the sentence representations of P-tuning compared to fine-tuning to help understand the above behaviour, and reveal how P-tuning generally presents a more limited ability to separate the sentence embeddings from different classes of augmented data. In addition, it displays poorer performance on heavily altered data. However, we demonstrate that by adding a simple contrastive loss function it can help mitigate such issues for prefix tuning, resulting in sizable improvements to augmented data performance.


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Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework
Junfan Chen | Richong Zhang | Yongyi Mao | Hongyu Guo | Jie Xu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Distant supervision for relation extraction enables one to effectively acquire structured relations out of very large text corpora with less human efforts. Nevertheless, most of the prior-art models for such tasks assume that the given text can be noisy, but their corresponding labels are clean. Such unrealistic assumption is contradictory with the fact that the given labels are often noisy as well, thus leading to significant performance degradation of those models on real-world data. To cope with this challenge, we propose a novel label-denoising framework that combines neural network with probabilistic modelling, which naturally takes into account the noisy labels during learning. We empirically demonstrate that our approach significantly improves the current art in uncovering the ground-truth relation labels.


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Syntax Encoding with Application in Authorship Attribution
Richong Zhang | Zhiyuan Hu | Hongyu Guo | Yongyi Mao
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We propose a novel strategy to encode the syntax parse tree of sentence into a learnable distributed representation. The proposed syntax encoding scheme is provably information-lossless. In specific, an embedding vector is constructed for each word in the sentence, encoding the path in the syntax tree corresponding to the word. The one-to-one correspondence between these “syntax-embedding” vectors and the words (hence their embedding vectors) in the sentence makes it easy to integrate such a representation with all word-level NLP models. We empirically show the benefits of the syntax embeddings on the Authorship Attribution domain, where our approach improves upon the prior art and achieves new performance records on five benchmarking data sets.


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A Deep Network with Visual Text Composition Behavior
Hongyu Guo
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

While natural languages are compositional, how state-of-the-art neural models achieve compositionality is still unclear. We propose a deep network, which not only achieves competitive accuracy for text classification, but also exhibits compositional behavior. That is, while creating hierarchical representations of a piece of text, such as a sentence, the lower layers of the network distribute their layer-specific attention weights to individual words. In contrast, the higher layers compose meaningful phrases and clauses, whose lengths increase as the networks get deeper until fully composing the sentence.


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DAG-Structured Long Short-Term Memory for Semantic Compositionality
Xiaodan Zhu | Parinaz Sobhani | Hongyu Guo
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


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Multi-level Evaluation for Machine Translation
Boxing Chen | Hongyu Guo | Roland Kuhn
Proceedings of the Tenth Workshop on Statistical Machine Translation

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NRC: Infused Phrase Vectors for Named Entity Recognition in Twitter
Colin Cherry | Hongyu Guo | Chengbi Dai
Proceedings of the Workshop on Noisy User-generated Text

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Neural Networks for Integrating Compositional and Non-compositional Sentiment in Sentiment Composition
Xiaodan Zhu | Hongyu Guo | Parinaz Sobhani
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

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The Unreasonable Effectiveness of Word Representations for Twitter Named Entity Recognition
Colin Cherry | Hongyu Guo
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Representation Based Translation Evaluation Metrics
Boxing Chen | Hongyu Guo
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)


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An Empirical Study on the Effect of Negation Words on Sentiment
Xiaodan Zhu | Hongyu Guo | Saif Mohammad | Svetlana Kiritchenko
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)