论文标题
用双句子编码的有效意图检测
Efficient Intent Detection with Dual Sentence Encoders
论文作者
论文摘要
在新域中构建对话系统并随着添加功能而需要资源有效的模型，这些模型在低数据制度下（即，在几个弹片设置中）。在这些要求的激励下，我们引入了以验证的双句子编码器（例如使用和转换）为支持的意图检测方法。我们证明了所提出的意图检测器的有用性和广泛的适用性，表明：1）它们基于微调的整个Bert-large模型或将BERT用作三个不同意图检测数据集的固定黑盒编码器； 2）在几个射击设置中，收益特别明显（即，每个意图只有10或30个带注释的示例）； 3）我们的意图探测器可以在单个CPU的几分钟内进行训练； 4）它们在不同的高参数设置中稳定。为了促进和民主化研究的重点是意图检测，我们发布了我们的代码，以及一个新的挑战性单域意图检测数据集，其中包括13,083个带注释的示例，超过77个意图。
Building conversational systems in new domains and with added functionality requires resource-efficient models that work under low-data regimes (i.e., in few-shot setups). Motivated by these requirements, we introduce intent detection methods backed by pretrained dual sentence encoders such as USE and ConveRT. We demonstrate the usefulness and wide applicability of the proposed intent detectors, showing that: 1) they outperform intent detectors based on fine-tuning the full BERT-Large model or using BERT as a fixed black-box encoder on three diverse intent detection data sets; 2) the gains are especially pronounced in few-shot setups (i.e., with only 10 or 30 annotated examples per intent); 3) our intent detectors can be trained in a matter of minutes on a single CPU; and 4) they are stable across different hyperparameter settings. In hope of facilitating and democratizing research focused on intention detection, we release our code, as well as a new challenging single-domain intent detection dataset comprising 13,083 annotated examples over 77 intents.
