Yung-Sung Chuang


2022

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DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings
Yung-Sung Chuang | Rumen Dangovski | Hongyin Luo | Yang Zhang | Shiyu Chang | Marin Soljacic | Shang-Wen Li | Scott Yih | Yoon Kim | James Glass
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We propose DiffCSE, an unsupervised contrastive learning framework for learning sentence embeddings. DiffCSE learns sentence embeddings that are sensitive to the difference between the original sentence and an edited sentence, where the edited sentence is obtained by stochastically masking out the original sentence and then sampling from a masked language model. We show that DiffSCE is an instance of equivariant contrastive learning, which generalizes contrastive learning and learns representations that are insensitive to certain types of augmentations and sensitive to other “harmful” types of augmentations. Our experiments show that DiffCSE achieves state-of-the-art results among unsupervised sentence representation learning methods, outperforming unsupervised SimCSE by 2.3 absolute points on semantic textual similarity tasks.

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Recent Advances in Pre-trained Language Models: Why Do They Work and How Do They Work
Cheng-Han Chiang | Yung-Sung Chuang | Hung-yi Lee
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: Tutorial Abstracts

Pre-trained language models (PLMs) are language models that are pre-trained on large-scaled corpora in a self-supervised fashion. These PLMs have fundamentally changed the natural language processing community in the past few years. In this tutorial, we aim to provide a broad and comprehensive introduction from two perspectives: why those PLMs work, and how to use them in NLP tasks. The first part of the tutorial shows some insightful analysis on PLMs that partially explain their exceptional downstream performance. The second part first focuses on emerging pre-training methods that enable PLMs to perform diverse downstream tasks and then illustrates how one can apply those PLMs to downstream tasks under different circumstances. These circumstances include fine-tuning PLMs when under data scarcity, and using PLMs with parameter efficiency. We believe that attendees of different backgrounds would find this tutorial informative and useful.

2021

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Mitigating Biases in Toxic Language Detection through Invariant Rationalization
Yung-Sung Chuang | Mingye Gao | Hongyin Luo | James Glass | Hung-yi Lee | Yun-Nung Chen | Shang-Wen Li
Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)

Automatic detection of toxic language plays an essential role in protecting social media users, especially minority groups, from verbal abuse. However, biases toward some attributes, including gender, race, and dialect, exist in most training datasets for toxicity detection. The biases make the learned models unfair and can even exacerbate the marginalization of people. Considering that current debiasing methods for general natural language understanding tasks cannot effectively mitigate the biases in the toxicity detectors, we propose to use invariant rationalization (InvRat), a game-theoretic framework consisting of a rationale generator and a predictor, to rule out the spurious correlation of certain syntactic patterns (e.g., identity mentions, dialect) to toxicity labels. We empirically show that our method yields lower false positive rate in both lexical and dialectal attributes than previous debiasing methods.

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Investigating the Reordering Capability in CTC-based Non-Autoregressive End-to-End Speech Translation
Shun-Po Chuang | Yung-Sung Chuang | Chih-Chiang Chang | Hung-yi Lee
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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Dual Inference for Improving Language Understanding and Generation
Shang-Yu Su | Yung-Sung Chuang | Yun-Nung Chen
Findings of the Association for Computational Linguistics: EMNLP 2020

Natural language understanding (NLU) and Natural language generation (NLG) tasks hold a strong dual relationship, where NLU aims at predicting semantic labels based on natural language utterances and NLG does the opposite. The prior work mainly focused on exploiting the duality in model training in order to obtain the models with better performance. However, regarding the fast-growing scale of models in the current NLP area, sometimes we may have difficulty retraining whole NLU and NLG models. To better address the issue, this paper proposes to leverage the duality in the inference stage without the need of retraining. The experiments on three benchmark datasets demonstrate the effectiveness of the proposed method in both NLU and NLG, providing the great potential of practical usage.

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Lifelong Language Knowledge Distillation
Yung-Sung Chuang | Shang-Yu Su | Yun-Nung Chen
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

It is challenging to perform lifelong language learning (LLL) on a stream of different tasks without any performance degradation comparing to the multi-task counterparts. To address this issue, we present Lifelong Language Knowledge Distillation (L2KD), a simple but efficient method that can be easily applied to existing LLL architectures in order to mitigate the degradation. Specifically, when the LLL model is trained on a new task, we assign a teacher model to first learn the new task, and pass the knowledge to the LLL model via knowledge distillation. Therefore, the LLL model can better adapt to the new task while keeping the previously learned knowledge. Experiments show that the proposed L2KD consistently improves previous state-of-the-art models, and the degradation comparing to multi-task models in LLL tasks is well mitigated for both sequence generation and text classification tasks.

2019

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Towards Understanding of Medical Randomized Controlled Trials by Conclusion Generation
Alexander Te-Wei Shieh | Yung-Sung Chuang | Shang-Yu Su | Yun-Nung Chen
Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)

Randomized controlled trials (RCTs) represent the paramount evidence of clinical medicine. Using machines to interpret the massive amount of RCTs has the potential of aiding clinical decision-making. We propose a RCT conclusion generation task from the PubMed 200k RCT sentence classification dataset to examine the effectiveness of sequence-to-sequence models on understanding RCTs. We first build a pointer-generator baseline model for conclusion generation. Then we fine-tune the state-of-the-art GPT-2 language model, which is pre-trained with general domain data, for this new medical domain task. Both automatic and human evaluation show that our GPT-2 fine-tuned models achieve improved quality and correctness in the generated conclusions compared to the baseline pointer-generator model. Further inspection points out the limitations of this current approach and future directions to explore.