Title: Towards Transparency in Coreference Resolution: A Quantum-Inspired Approach Show Chinese title

Title: 迈向共指消解的透明性：一种量子启发的方法

Abstract: Guided by grammatical structure, words compose to form sentences, and guided by discourse structure, sentences compose to form dialogues and documents. The compositional aspect of sentence and discourse units is often overlooked by machine learning algorithms. A recent initiative called Quantum Natural Language Processing (QNLP) learns word meanings as points in a Hilbert space and acts on them via a translation of grammatical structure into Parametrised Quantum Circuits (PQCs). Previous work extended the QNLP translation to discourse structure using points in a closure of Hilbert spaces. In this paper, we evaluate this translation on a Winograd-style pronoun resolution task. We train a Variational Quantum Classifier (VQC) for binary classification and implement an end-to-end pronoun resolution system. The simulations executed on IBMQ software converged with an F1 score of 87.20%. The model outperformed two out of three classical coreference resolution systems and neared state-of-the-art SpanBERT. A mixed quantum-classical model yet improved these results with an F1 score increase of around 6%. Show Chinese abstract

Abstract: 基于语法结构，单词组成句子，而基于话语结构，句子组成对话和文档。 句子和话语单元的组合方面常常被机器学习算法所忽视。 最近一项名为量子自然语言处理（QNLP）的举措将词义作为希尔伯特空间中的点，并通过将语法结构转化为参数化量子电路（PQCs）来作用于它们。 先前的工作使用希尔伯特空间的闭包中的点，将QNLP的翻译扩展到话语结构。 在本文中，我们在一个Winograd风格的代词消解任务上评估了这种翻译。 我们训练了一个变分量子分类器（VQC）进行二分类，并实现了一个端到端的代词消解系统。 在IBMQ软件上执行的模拟达到了87.20%的F1分数。 该模型优于三个经典共指解析系统中的两个，并接近最先进的SpanBERT。 一种混合量子-经典模型进一步提高了这些结果，F1分数增加了约6%。