论文标题
适应验证的变压器到晶格，以了解口语理解
Adapting Pretrained Transformer to Lattices for Spoken Language Understanding
论文作者
论文摘要
晶格是编码多个假设的紧凑表示，例如语音识别结果或不同的单词分割。结果表明，编码晶格而不是由自动语音识别器（ASR）产生的1好的结果提高口语理解的性能（SLU）。最近，具有变压器体系结构的验证语言模型已经取得了自然语言理解的最新结果，但是尚未探索它们编码格的能力。因此，本文旨在将经过预定的变压器调整为晶格输入，以执行专门针对口语的理解任务。我们在基准ATIS数据集上进行的实验表明，用晶格输入进行微调预验的变压器可以明显改善以1好的结果进行微调。进一步的评估证明了我们在不同声学条件下方法的有效性。我们的代码可在https://github.com/miulab/lattice-slu上找到
Lattices are compact representations that encode multiple hypotheses, such as speech recognition results or different word segmentations. It is shown that encoding lattices as opposed to 1-best results generated by automatic speech recognizer (ASR) boosts the performance of spoken language understanding (SLU). Recently, pretrained language models with the transformer architecture have achieved the state-of-the-art results on natural language understanding, but their ability of encoding lattices has not been explored. Therefore, this paper aims at adapting pretrained transformers to lattice inputs in order to perform understanding tasks specifically for spoken language. Our experiments on the benchmark ATIS dataset show that fine-tuning pretrained transformers with lattice inputs yields clear improvement over fine-tuning with 1-best results. Further evaluation demonstrates the effectiveness of our methods under different acoustic conditions. Our code is available at https://github.com/MiuLab/Lattice-SLU
