Papers by Bin Ni

2 papers
A Unified Speaker Adaptation Approach for ASR (2021.emnlp-main)

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Challenge: Adapting a model to target speakers requires a lot of compute and may cause catastrophic forgetting to the existing speakers.
Approach: They propose a unified speaker adaptation approach consisting of feature adaptation and model adaptation.
Outcome: The proposed model outperforms baseline models with 20.58% relative WER reduction and surpasses finetuning method by 2.54% on target speaker adaptation.
BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models (2023.findings-acl)

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Challenge: Existing methods to construct knowledge graphs are limited to a small set of relations due to manual cost or restrictions in text corpus.
Approach: They propose to automatically construct knowledge graphs (KGs) of diverse new relations from pretrained language models that accept knowledge queries with prompts.
Outcome: The proposed framework extracts knowledge of over 400 new relations from pretrained language models, including RoBERTaNet, with minimal input of a relation definition and a few shot of example entity pairs.

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