Design of Belarusian and Russian natural language interfaces for online help systems

O b j e c t i v e s. The main goal of the work is a research of the natural language user interfaces and the developmentof a prototype of such an interface. The prototype is a bilingual Russian and Belarusian question-and-answer dialogue system. The research of the natural language interfaces was conducted in terms of the use of natural language for interaction between a user and a computer system. The main problems here are the ambiguity of natural language and the difficulties in the design of natural language interfaces that meet user expectations.M e t ho d s. The main principles of modelling the natural language user interfaces are considered. As an intelligent system, it consists of a database, knowledge machine and a user interface. Speech recognition and speech synthesis components make natural language interfaces more convenient from the point of view of usability.R e s u l t s. The description of the prototype of a natural language interface for a question-and-answer intelligent system is presented. The model of the prototype includes speech-to-text and text-to-speech Belarusian and Russian subsystems, generation of responses in the form of the natural language and formal text.An additional component is natural Belarusian and Russian voice input. Some of the data, required for human voice recognition, are stored as knowledge in the knowledge base or created on the basis of existing knowledge. Another important component is Belarusian and Russian voice output. This component is the top required for making the natural language interface more user-friendly.Co n c l u s i o n. The article presents the research of natural language user interfaces, the result of which provides the development and description of the prototype of the natural language interface for the intelligent question- and-answer system.

Empowering Natural Language Interfaces to Databases with Aggregations

A Natural Language Interface to Database (NLIDB) refers to a database interface that translates a question asked in natural language into a structured query. Aggregation questions express aggregation functions, such as count, sum, average, minimum and maximum, and optionally a group by clause and a having clause. NLIDBs deliver good results for standard questions but usually do not deal with aggregation questions. The main contribution of this article is a generic module, called GLAMORISE (GeneraL Aggregation MOdule using a RelatIonal databaSE), that extends NLIDBs to cope with aggregation questions. GLAMORISE covers aggregations with ambiguities, timescale differences, aggregations in multiple attributes, the use of superlative adjectives, basic recognition of measurement units, and aggregations in attributes with compound names.

API2CAN: a dataset & service for canonical utterance generation for REST APIs

Objectives Recently natural language interfaces (e.g., chatbots) have gained enormous attention. Such interfaces execute underlying application programming interfaces (APIs) based on the user's utterances to perform tasks (e.g., reporting weather). Supervised approaches for building such interfaces rely upon a large set of user utterances paired with APIs. Collecting such pairs is typically starts with obtaining initial utterances for a given API method. Generating initial utterances can be considered as a machine translation task in which an API method is translated into an utterance. However, the key challenge is the lack of training samples for training domain-independent translation models. In this paper, we propose a dataset for training supervised models to generate initial utterances for APIs. Data description The dataset contains 14,370 pairs of API methods and utterances. It is built automatically by converting method descriptions of a large number of APIs to user utterances; and it is cleaned manually to ensure quality. The dataset is also accompanied with a set of microservices (e.g., translating API methods to utterances) which can facilitate the process of collecting training samples for building natural language interfaces.

Welcome to AI Matters 7(2)

Welcome to the second issue of this year's AI Matters Newsletter. We start with a report on upcoming SIGAI Events by Dilini Samarasinghe and Conference reports by Louise Dennis, our conference coordination officer. In our regular Education column, Carolyn Rosé discusses the role of AI in education in a post-pandemic reality. We then bring you our regular Policy column, where Larry Medsker covers interesting and timely discussions on AI policy, for example whether governments should play a role in reducing algorithmic bias. This issue closes with an article contribution from Li Dong, one of the runner-ups in the latest AAIS/SIGAI dissertation award, on the use neural models to build natural language interfaces.

Learning natural language interfaces with neural models

Language is the primary and most natural means of communication for humans. The learning curve of interacting with various services (e.g., digital assistants, and smart appliances) would be greatly reduced if we could talk to machines using human language. However, in most cases computers can only interpret and execute formal languages.

Issues in the Syntactic Parsing of Queries for a Natural Language Interface to Databases

In this chapter, an exhaustive parser is presented. The parser was developed to be used in a natural language interface to databases (NLIDB) project. This chapter includes a brief description of state-of-the-art NLIDBs, including a description of the methods used and the performance of some interfaces. Some of the general problems in natural language interfaces to databases are also explained. The exhaustive parser was developed, aiming at improving the overall performance of the interface; therefore, the interface is also briefly described. This chapter also presents the drawbacks discovered during the experimental tests of the parser, which show that it is unsuitable for improving the NLIDB performance.

Two New Challenging Resources to Evaluate Natural Language Interfaces to Databases Generated Based on Geobase and Geoquery

Databases and corpora are essential resources to evaluate the performance of Natural Language Interfaces to Databases (NLIDB). The Geobase database and the Geoquery corpus (Geoquery250 and Geoquery880) are among the most commonly used. In this chapter, the authors analyze both resources to offer two elaborate resources: 1) N-Geobase, which is a relational database, and 2) the corpus Geoquery270. The former follows the standard normalization procedure, then N-Geobase has a schema similar to enterprise databases. Geoquery270 consists of 270 queries selected from Geoquery880, preserving the same kind of natural language problems as Geoquery880, but with more challenging issues for an NLIDB than Geoquery250. To evaluate the new resources, they compared the performance of the NLIDB using Geoquery270 and Geoquery250. The results indicated that Geoquery270 was the harder corpus, while Geoquery250 is the easier one. Consequently, this chapter offers a broader range of resources to NLIDB designers.

Natural Language Interfaces to Databases

This chapter consists of an update of a previous publication. Specifically, the chapter aims at describing the most decisive advances in NLIDBs of this decade. Unlike many surveys on NLIDBs, for this chapter, the NLIDBs will be selected according to three relevance criteria: performance (i.e., percentage of correctly answered queries), soundness of the experimental evaluation, and the number of citations. To this end, the chapter will also include a brief review of the most widely used performance measures and query corpora for testing NLIDBs.

The history and recent advances of Natural Language Interfaces for Databases Querying

Databases have been always the most important topic in the study of information systems, and an indispensable tool in all information management systems. However, the extraction of information stored in these databases is generally carried out using queries expressed in a computer language, such as SQL (Structured Query Language). This generally has the effect of limiting the number of potential users, in particular non-expert database users who must know the database structure to write such requests. One solution to this problem is to use Natural Language Interface (NLI), to communicate with the database, which is the easiest way to get information. So, the appearance of Natural Language Interfaces for Databases (NLIDB) is becoming a real need and an ambitious goal to translate the user’s query given in Natural Language (NL) into the corresponding one in Database Query Language (DBQL). This article provides an overview of the state of the art of Natural Language Interfaces as well as their architecture. Also, it summarizes the main recent advances on the task of Natural Language Interfaces for databases.