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Articles by Luoxin Chen
Enhance robustness of sequence labelling with masked adversarial training
Adversarial training (AT) has shown strong regularization effects on deep learning algorithms by introducing small input perturbations to improve model robustness. In language tasks, adversarial training brings word-level robustness by adding input noise, which is beneficial for text classification. However, it lacks sufficient contextual information enhancement and thus is less useful for sequence labelling tasks such as chunking and named entity recognition (NER).
Towards an ASR error robust spoken language understanding system
A modern Spoken Language Understanding (SLU) system usually contains two sub-systems, Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU),where ASR transforms voice signal to text form and NLU provides intent classification and slot filling from the text. In practice,such decoupled ASR/NLU design facilitates fast model iteration for both components. However, this makes downstream NLU susceptible to errors from the upstream ASR, causing significant performance degradation.
ASR n-best fusion nets
Current spoken language understanding systems heavily rely on the best hypothesis (ASR 1-best) generated by automatic speech recognition, which is used as the input for downstream models such as natural language understanding (NLU) modules. However, the potential errors and misrecognition in ASR 1-best raise challenges to NLU. It is usually difficult for NLU models to recover from ASR errors without additional signals, which leads to suboptimal SLU performance.
Industry scale semi-supervised learning for natural language understanding
This paper presents a production Semi-Supervised Learning (SSL) pipeline based on the student-teacher framework, which leverages millions of unlabeled examples to improve Natural Language Understanding (NLU) tasks.
Using unlabeled data to improve sequence labeling
Virtual adversarial training (VAT) is a way to improve machine learning systems by creating difficult-to-classify training examples through the addition of noise to unlabeled data. It has had great success on both image classification tasks and text classification tasks, such as determining the sentiment of a review or the topic of an article. It’s not as well adapted, however, to sequence-labeling tasks, in which each word of an input phrase gets its own label.
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