论文标题
对话系统中成语的向量表示
Vector Representations of Idioms in Conversational Systems
论文作者
论文摘要
我们在这项研究中证明了接受成语或象征性语言训练的开放域对话系统会对包含成语提示的提示产生更合适的响应。成语是许多文化中多种语言的日常语音的一部分，但是除了对话型AI外，它们对许多自然语言处理（NLP）系统构成了巨大的挑战，涉及信息检索（IR）和机器翻译（MT）等任务。我们利用潜在的惯用表达（PIE） - 英语成语语料库来进行我们研究的两个任务：分类和对话的产生。我们通过使用SOTA T5模型在分类任务上实现了98％宏F1分数的最新结果（SOTA）。我们尝试了SOTA对话模型的三个实例，即对话生成预训练的变压器（对话），以进行对话。使用自动的度量困惑和人类评估评估他们的性能。结果表明，与未经习惯语料库训练的类似模型相比，对成语语料库进行训练的模型对包含成语的提示产生了更多的拟合响应。我们在HuggingFace Hub上为模型检查点/演示和代码贡献了公众访问。
We demonstrate, in this study, that an open-domain conversational system trained on idioms or figurative language generates more fitting responses to prompts containing idioms. Idioms are part of everyday speech in many languages, across many cultures, but they pose a great challenge for many Natural Language Processing (NLP) systems that involve tasks such as Information Retrieval (IR) and Machine Translation (MT), besides conversational AI. We utilize the Potential Idiomatic Expression (PIE)-English idioms corpus for the two tasks that we investigate: classification and conversation generation. We achieve state-of-the-art (SoTA) result of 98% macro F1 score on the classification task by using the SoTA T5 model. We experiment with three instances of the SoTA dialogue model, Dialogue Generative Pre-trained Transformer (DialoGPT), for conversation generation. Their performances are evaluated using the automatic metric perplexity and human evaluation. The results show that the model trained on the idiom corpus generates more fitting responses to prompts containing idioms 71.9% of the time, compared to a similar model not trained on the idioms corpus. We contribute the model checkpoint/demo and code on the HuggingFace hub for public access.
