Papers by Haode Qi
Multilingual BERT Post-Pretraining Alignment (2021.naacl-main)
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| Challenge: | Recent work improves on the success of monolingual pretrained language models by adding cross-lingual tasks that always involve English. |
| Approach: | They propose a method to align multilingual contextual embeddings as a post-pretraining step for improved cross-lingual transferability of pretrained language models. |
| Outcome: | The proposed model outperforms XLM-R_Base on translation-train tasks while using less parallel data and fewer parameters. |
Benchmarking Commercial Intent Detection Services with Practice-Driven Evaluations (2021.naacl-industry)
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| Challenge: | Intent detection models require large amounts of labeled data to achieve high accuracy, and in practical scenarios it is more common to find small, unbalanced, and noisy datasets. |
| Approach: | They benchmark intent detection methods on a variety of datasets and found that Watson Assistant's model outperforms other commercial solutions. |
| Outcome: | The proposed model outperforms pretrained language models on a variety of datasets while requiring only a fraction of computational resources and training data. |
Distinguish Sense from Nonsense: Out-of-Scope Detection for Virtual Assistants (2022.emnlp-industry)
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| Challenge: | Out of Scope (OOS) detection is a problem with chatbots that cannot make sense of a query . a real-world solution to this problem is to identify out-of-domain queries . |
| Approach: | They propose a simple yet effective OOS detection method that outperforms standard methods . they propose analyzing data from an enterprise virtual assistant platform to test the method . |
| Outcome: | The proposed method outperforms standard OOS detection methods in a real-world deployment of virtual assistants. |