论文标题
可解释的文本相似性的逻辑约束指针网络
Logic Constrained Pointer Networks for Interpretable Textual Similarity
论文作者
论文摘要
在文本中系统地发现语义关系是自然语言处理中的一个重要且广泛研究的领域，并具有各种任务，例如，语义相似性等各种任务。已经提出了通过子序列比对的句子级别分数的可分解性，以使模型更加可解释。我们研究句子组成部分的问题，导致语义文本相似性的可解释模型。在本文中，我们介绍了一个具有哨兵门控函数的基于新颖的指针网络模型，以使组成块与使用BERT表示。我们通过损失函数改善了这个基本模型，以同样惩罚两个句子的未对准，以确保对齐方式是双向的。最后，为了用结构化的外部知识指导网络，我们基于概念网和句法知识介绍了一阶逻辑约束。该模型在基准的Semeval数据集上实现了97.73和96.32的F1分数，用于块对齐任务，显示了对现有解决方案的巨大改进。源代码可在https://github.com/manishb89/interpretable_sentence_simarility上获得
Systematically discovering semantic relationships in text is an important and extensively studied area in Natural Language Processing, with various tasks such as entailment, semantic similarity, etc. Decomposability of sentence-level scores via subsequence alignments has been proposed as a way to make models more interpretable. We study the problem of aligning components of sentences leading to an interpretable model for semantic textual similarity. In this paper, we introduce a novel pointer network based model with a sentinel gating function to align constituent chunks, which are represented using BERT. We improve this base model with a loss function to equally penalize misalignments in both sentences, ensuring the alignments are bidirectional. Finally, to guide the network with structured external knowledge, we introduce first-order logic constraints based on ConceptNet and syntactic knowledge. The model achieves an F1 score of 97.73 and 96.32 on the benchmark SemEval datasets for the chunk alignment task, showing large improvements over the existing solutions. Source code is available at https://github.com/manishb89/interpretable_sentence_similarity
