Theoretically founded algorithms for the automatic production of analogy tests in NLP

在第二年，并行进行了任务(A)至(C)的工作。(A)尝试了两种方法来投射字符串的矢量表示：第一种直接使用Parikh矢量表示，第二种是一个隐层神经网络。对不同数据的召回率和精确度进行了测量。(B)探索了几个方向。(b.1)进行了一系列将实值向量逼近为整值向量的实验。使用了几种语言的几个类比测试集。使用了在第一个财政年度实施的加速方案的新版本。在任何设置中都找不到表示向量之间的类比的平行四边形。这一结果已在一次国际会议上发表。(b.2)完成了将单词类比测试集中的单词转换为其定义，即句子的工作。使用由单词类比引起的具有类比结构的定义来微调嵌入对比学习的句子空间。这种微调的空间在语义相似性任务中提供了更好的性能。(B.3)已经编写了从给定单词周围的子空间中自动提取一系列类比的程序。初步实验是在经典例子上进行的。所获得的类比几乎总是形式上的，尽管它们起源于使用分布假设建立的嵌入空间。(C)程序的并行化被认为在第一个财政年度完成。

During the second year, work on the tasks (a) to (c) have been pursued in parallel.(a) Two approaches were tried to cast vector representations of strings: The first one directly used Parikh vector representations and the second one a one hidden-layer neural network. Recall and precision were measured on various data.(b) Several directions were explored. (b.1) A series of experiments to approximate real-valued vectors to integer-valued vectors was run. Several analogy test sets in several languages were used. The new version of the programs with acceleration, implemented during the first fiscal year, was used. No parallelogram representing analogies between vectors can be discovered in none of the settings. This result has been published in a an international conference. (b.2) Work on casting words from word analogy test sets into their definitions, i.e., sentences, was done. The definitions with the analogical structure induced by the word analogies were used to fine-tune a sentence embedding space with contrastive learning. Such fine-tuned spaces delivered better performance in semantic similarity tasks. (b.3) Programs have been written to automatically extract series of analogies from a subspace around a given word. Preliminary experiments were run on classical examples. The obtained analogies are almost always formal, although they originate from an embedding space built using the distributional hypothesis.(c) Parallelisation of programs is considered as finished in the first fiscal year.