no code implementations • 24 May 2023 • Yau-Shian Wang, Ta-Chung Chi, Ruohong Zhang, Yiming Yang
We present PESCO, a novel contrastive learning framework that substantially improves the performance of zero-shot text classification.
1 code implementation • 24 Apr 2023 • Ruohong Zhang, Yau-Shian Wang, Yiming Yang
To overcome these limitations, we introduce a novel method, namely GenCo, which leverages the strong generative power of LLMs to assist in training a smaller and more adaptable language model.
1 code implementation • 11 Nov 2022 • Yau-Shian Wang, Ashley Wu, Graham Neubig
The performance can be further enhanced when cross-lingual NLI data is available.
no code implementations • 24 May 2022 • Yau-Shian Wang, Yingshan Chang
It is a long-known risk that language models (LMs), once trained on corpus containing undesirable content, have the power to manifest biases and toxicity.
no code implementations • 2 Apr 2022 • Ruohong Zhang, Yau-Shian Wang, Yiming Yang, Tom Vu, Likun Lei
Extreme multi-label text classification (XMTC) is the task of tagging each document with the relevant labels from a very large space of predefined categories.
Multi Label Text Classification Multi-Label Text Classification +1
no code implementations • 2 Apr 2022 • Ruohong Zhang, Yau-Shian Wang, Yiming Yang, Donghan Yu, Tom Vu, Likun Lei
Extreme Multi-label Text Classification (XMTC) has been a tough challenge in machine learning research and applications due to the sheer sizes of the label spaces and the severe data scarce problem associated with the long tail of rare labels in highly skewed distributions.
Multi Label Text Classification Multi-Label Text Classification +3
no code implementations • 12 Oct 2021 • Ting-Rui Chiang, Yi-Ting Yeh, Ta-Chung Chi, Yau-Shian Wang
ALFRED is a recently proposed benchmark that requires a model to complete tasks in simulated house environments specified by instructions in natural language.
no code implementations • 11 Jul 2020 • Hung-Yi Lee, Cheng-Hao Ho, Chien-Fu Lin, Chiung-Chih Chang, Chih-Wei Lee, Yau-Shian Wang, Tsung-Yuan Hsu, Kuan-Yu Chen
Conventional seq2seq chatbot models attempt only to find sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the output sentences.
no code implementations • 28 Apr 2020 • Yau-Shian Wang, Hung-Yi Lee, Yun-Nung Chen
Also, the performance is on par with a recently proposed weakly-supervised text classification method.
3 code implementations • IJCNLP 2019 • Yau-Shian Wang, Hung-Yi Lee, Yun-Nung Chen
This paper proposes Tree Transformer, which adds an extra constraint to attention heads of the bidirectional Transformer encoder in order to encourage the attention heads to follow tree structures.
1 code implementation • EMNLP 2018 • Yau-Shian Wang, Hung-Yi Lee
The generator encodes the input text into a shorter word sequence, and the reconstructor recovers the generator input from the generator output.
no code implementations • 27 Sep 2018 • Yau-Shian Wang, Yun-Nung Chen, Hung-Yi Lee
Learning discrete representations of data and then generating data from the discovered representations have been increasingly studied because the obtained discrete representations can benefit unsupervised learning.
no code implementations • 7 Apr 2018 • Chih-Wei Lee, Yau-Shian Wang, Tsung-Yuan Hsu, Kuan-Yu Chen, Hung-Yi Lee, Lin-shan Lee
Conventional seq2seq chatbot models only try to find the sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the output sentences.