Fei Liu

Research Scientist at Google
Authored Publications
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    DAMP: Doubly Aligned Multilingual Parser for Task-Oriented Dialogue
    William Held
    Rahul Goel
    Diyi Yang
    Rushin Shah
    Association for Computational Linguistics(2023)
    Preview abstract Modern virtual assistants use internal semantic parsing engines to convert user utterances to actionable commands. However, prior work has demonstrated that semantic parsing is a difficult multilingual transfer task with low transfer efficiency compared to other tasks. In global markets such as India and Latin America, this is a critical issue as switching between languages is prevalent for bilingual users. In this work we dramatically improve the zero-shot performance of a multilingual and codeswitched semantic parsing system using two stages of multilingual alignment. First, we show that constrastive alignment pretraining improves both English performance and transfer efficiency. We then introduce a constrained optimization approach for hyperparameter-free adversarial alignment during finetuning. Our Doubly Aligned Multilingual Parser (DAMP) improves mBERT transfer performance by 3x, 6x, and 81x on the Spanglish, Hinglish and Multilingual Task Oriented Parsing benchmarks respectively and outperforms XLM-R and mT5-Large using 3.2x fewer parameters. View details
    Reducing Model Churn: Stable Re-training of Conversational Agents
    Rahul Goel
    SIGDIAL, Association for Computational Linguistics(2022)
    Preview abstract Retraining modern deep learning systems can lead to variations in model performance even when trained using the same data and hyperparameters by simply using different random seeds. This phenomenon is known as model churn or model jitter. This issue is often exacerbated in real world settings, where noise may be introduced in the data collection process. In this work we tackle the problem of stable retraining with a novel focus on structured prediction for conversational semantic parsing. We first quantify the model churn by introducing metrics for agreement between predictions across multiple re-trainings. Next, we devise realistic scenarios for noise injection and demonstrate the effectiveness of various churn reduction techniques such as ensembling and distillation. Lastly, we discuss practical tradeoffs between such techniques and show that codistillation provides a sweet spot in terms of churn reduction with only a modest increase in resource usage. View details