An Unsupervised Aspect-Aware Recommendation Model with Explanation Text Generation

Review based recommendation utilizes both users’ rating records and the associated reviews for recommendation. Recently, with the rapid demand for explanations of recommendation results, reviews are used to train the encoder–decoder models for explanation text generation. As most of the reviews are general text without detailed evaluation, some researchers leveraged auxiliary information of users or items to enrich the generated explanation text. Nevertheless, the auxiliary data is not available in most scenarios and may suffer from data privacy problems. In this article, we argue that the reviews contain abundant semantic information to express the users’ feelings for various aspects of items, while these information are not fully explored in current explanation text generation task. To this end, we study how to generate more fine-grained explanation text in review based recommendation without any auxiliary data. Though the idea is simple, it is non-trivial since the aspect is hidden and unlabeled. Besides, it is also very challenging to inject aspect information for generating explanation text with noisy review input. To solve these challenges, we first leverage an advanced unsupervised neural aspect extraction model to learn the aspect-aware representation of each review sentence. Thus, users and items can be represented in the aspect space based on their historical associated reviews. After that, we detail how to better predict ratings and generate explanation text with the user and item representations in the aspect space. We further dynamically assign review sentences which contain larger proportion of aspect words with larger weights to control the text generation process, and jointly optimize rating prediction accuracy and explanation text generation quality with a multi-task learning framework. Finally, extensive experimental results on three real-world datasets demonstrate the superiority of our proposed model for both recommendation accuracy and explainability.

Relation-Aware Graph Transformer for SQL-to-Text Generation

Generating natural language descriptions for structured representation (e.g., a graph) is an important yet challenging task. In this work, we focus on SQL-to-text, a task that maps a SQL query into the corresponding natural language question. Previous work represents SQL as a sparse graph and utilizes a graph-to-sequence model to generate questions, where each node can only communicate with k-hop nodes. Such a model will degenerate when adapted to more complex SQL queries due to the inability to capture long-term and the lack of SQL-specific relations. To tackle this problem, we propose a relation-aware graph transformer (RGT) to consider both the SQL structure and various relations simultaneously. Specifically, an abstract SQL syntax tree is constructed for each SQL to provide the underlying relations. We also customized self-attention and cross-attention strategies to encode the relations in the SQL tree. Experiments on benchmarks WikiSQL and Spider demonstrate that our approach yields improvements over strong baselines.

Medical Books of the 17th–18th Centuries from the Siberian Archives: Structural-and-Grammatical Aspect

The article presents a comparative study of two medical books from the Siberian archives dating back to the 17 th –18 th centuries: Tobolsk Lechebnik (TL) kept in Tobolsk Book Depository, and Altai Lechebnik (AL) stored in Altai Museum of Local Lore – both stemming from the text of the medical book called "Prokhladnyi Vertograd (The Cool Garden)" from the collection of the Rumyantsev Museum (PV). Our findings show that Siberian medical books demonstrate different degrees of structural and grammatical transformation of the source PV text, conventionally considered by the authors of the research to be a list, which is chronologically closer to the original text. It was established that TL can be regarded as a list derived from the PV, and AL – a source reflecting a further stage in the process of text generation in the institutional medical discourse. We claim that the intentional and grammatical perspective of the medical text formation is associated with the modal variability of verbal lexemes: the prevailing in PV and TL personal verb forms reflect the presence of the subject of speech as an agent in special communication; in AL these are replaced by infinitives which transform the real modality of the message about an action "from experience" (in PV and TL) into a syntactic categorical imperative.It was also determined that the subject of the action expressed by personal verb forms is typically generalized (in this case, special actions of the doctor and the patient can be detected through the difference in the verbal lexemes). The subject is not grammatically defined with the infinitive verb forms.

Evaluation of Synthetic Datasets Generation for Intent Classification Tasks in Portuguese

A chatbot is an artificial intelligence based system aimed at chatting with users, commonly used as a virtual assistant to help people or answer questions. Intent classification is an essential task for chatbots where it aims to identify what the user wants in a certain dialogue. However, for many domains, little data are available to properly train those systems. In this work, we evaluate the performance of two methods to generate synthetic data for chatbots, one based on template questions and another based on neural text generation. We build four datasets that are used training chatbot components in the intent classification task. We intend to simulate the task of migrating a search-based portal to an interactive dialogue-based information service by using artificial datasets for initial model training. Our results show that template-based datasets are slightly superior to those neural-based generated in our application domain, however, neural-generated present good results and they are a viable option when one has limited access to domain experts to hand-code text templates.

Table to text generation with accurate content copying

Generating fluent, coherent, and informative text from structured data is called table-to-text generation. Copying words from the table is a common method to solve the “out-of-vocabulary” problem, but it’s difficult to achieve accurate copying. In order to overcome this problem, we invent an auto-regressive framework based on the transformer that combines a copying mechanism and language modeling to generate target texts. Firstly, to make the model better learn the semantic relevance between table and text, we apply a word transformation method, which incorporates the field and position information into the target text to acquire the position of where to copy. Then we propose two auxiliary learning objectives, namely table-text constraint loss and copy loss. Table-text constraint loss is used to effectively model table inputs, whereas copy loss is exploited to precisely copy word fragments from a table. Furthermore, we improve the text search strategy to reduce the probability of generating incoherent and repetitive sentences. The model is verified by experiments on two datasets and better results are obtained than the baseline model. On WIKIBIO, the result is improved from 45.47 to 46.87 on BLEU and from 41.54 to 42.28 on ROUGE. On ROTOWIRE, the result is increased by 4.29% on CO metric, and 1.93 points higher on BLEU.