skip to main content

Title: Structured Data Representation in Natural Language Interfaces
A Natural Language Interface (NLI) enables the use of human languages to interact with computer systems, including smart phones and robots. Compared to other types of interfaces, such as command line interfaces (CLIs) or graphical user interfaces (GUIs), NLIs stand to enable more people to have access to functionality behind databases or APIs as they only require knowledge of natural languages. Many NLI applications involve structured data for the domain (e.g., applications such as hotel booking, product search, and factual question answering.) Thus, to fully process user questions, in addition to natural language comprehension, understanding of structured data is also crucial for the model. In this paper, we study neural network methods for building Natural Language Interfaces (NLIs) with a focus on learning structure data representations that can generalize to novel data sources and schemata not seen at training time. Specifically, we review two tasks related to natural language interfaces: i) semantic parsing where we focus on text-to-SQL for database access, and ii) task-oriented dialog systems for API access. We survey representative methods for text-to-SQL and task-oriented dialog tasks, focusing on representing and incorporating structured data. Lastly, we present two of our original studies on structured data representation methods for NLIs to enable access to i) databases, and ii) visualization APIs.  more » « less
Award ID(s):
Author(s) / Creator(s):
Date Published:
Journal Name:
A Quarterly bulletin of the Computer Society of the IEEE Technical Committee on Data Engineering
Page Range / eLocation ID:
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. null (Ed.)
    Recent years have witnessed the enormous success of text representation learning in a wide range of text mining tasks. Earlier word embedding learning approaches represent words as fixed low-dimensional vectors to capture their semantics. The word embeddings so learned are used as the input features of task-specific models. Recently, pre-trained language models (PLMs), which learn universal language representations via pre-training Transformer-based neural models on large-scale text corpora, have revolutionized the natural language processing (NLP) field. Such pre-trained representations encode generic linguistic features that can be transferred to almost any text-related applications. PLMs outperform previous task-specific models in many applications as they only need to be fine-tuned on the target corpus instead of being trained from scratch. In this tutorial, we introduce recent advances in pre-trained text embeddings and language models, as well as their applications to a wide range of text mining tasks. Specifically, we first overview a set of recently developed self-supervised and weakly-supervised text embedding methods and pre-trained language models that serve as the fundamentals for downstream tasks. We then present several new methods based on pre-trained text embeddings and language models for various text mining applications such as topic discovery and text classification. We focus on methods that are weakly-supervised, domain-independent, language-agnostic, effective and scalable for mining and discovering structured knowledge from large-scale text corpora. Finally, we demonstrate with real world datasets how pre-trained text representations help mitigate the human annotation burden and facilitate automatic, accurate and efficient text analyses. 
    more » « less
  2. Recent years have witnessed the emerging of conversational systems, including both physical devices and mobile-based applications, such as Amazon Echo, Google Now, Microsoft Cortana, Apple Siri, and many others. Both the research community and industry believe that conversational systems will have a major impact on human-computer interaction, and specifically, the IR community has begun to focus on Conversational Search. Conversational search based on user-system dialog exhibits major differences from conventional search in that 1) the user and system can interact for multiple semantically coherent rounds on a task through natural language dialog, and 2) it becomes possible for the system to understand user needs or to help users clarify their needs by asking appropriate questions from the users directly. In this paper, we propose and evaluate a unified conversational search framework. Specifically, we define the major components for conversational search, assemble them into a unified framework, and test an implementation of the framework using a conversational product search scenario in Amazon. To accomplish this, we propose the Multi-Memory Network (MMN) architecture, which is end-to-end trainable based on large-scale collections of user reviews in e-commerce. The system is capable of asking aspect-based questions in the right order so as to understand user needs, while (personalized) search is conducted during the conversation and results are provided when the system feels confident. Experiments on real-world user purchasing data verified the advantages of conversational search against conventional search algorithms in terms of standard evaluation measures such as NDCG. 
    more » « less
  3. A natural language interface (NLI) to databases is an interface that translates a natural language question to a structured query that is executable by database management systems (DBMS). However, an NLI that is trained in the general domain is hard to apply in the spatial domain due to the idiosyncrasy and expressiveness of the spatial questions. Inspired by the machine comprehension model, we propose a spatial comprehension model that is able to recognize the meaning of spatial entities based on the semantics of the context. The spatial semantics learned from the spatial comprehension model is then injected to the natural language question to ease the burden of capturing the spatial-specific semantics. With our spatial comprehension model and information injection, our NLI for the spatial domain, named SpatialNLI, is able to capture the semantic structure of the question and translate it to the corresponding syntax of an executable query accurately. We also experimentally ascertain that SpatialNLI outperforms state-of-the-art methods. 
    more » « less
  4. Author Name Disambiguation (AND) is the task of clustering unique author names from publication records in scholarly or related databases. Although AND has been extensively studied and has served as an important preprocessing step for several tasks (e.g. calculating bibliometrics and scientometrics for authors), there are few publicly available tools for disambiguation in large-scale scholarly databases. Furthermore, most of the disambiguated data is embedded within the search engines of the scholarly databases, and existing application programming interfaces (APIs) have limited features and are often unavailable for users for various reasons. This makes it difficult for researchers and developers to use the data for various applications (e.g. author search) or research. Here, we design a novel, web-based, RESTful API for searching disambiguated authors, using the PubMed database as a sample application. We offer two type of queries, attribute-based queries and record-based queries which serve different purposes. Attribute-based queries retrieve authors with the attributes available in the database. We study different search engines to find the most appropriate one for processing attribute-based queries. Record-based queries retrieve authors that are most likely to have written a query publication provided by a user. To accelerate record-based queries, we develop a novel algorithm that has a fast record-to-cluster match. We show that our algorithm can accelerate the query by a factor of 4.01 compared to a baseline naive approach. 
    more » « less
  5. Jovanovic, Jelena ; Chounta, Irene-Angelica ; Uhomoibhi, James ; McLaren, Bruce (Ed.)
    Computer-supported education studies can perform two important roles. They can allow researchers to gather important data about student learning processes, and they can help students learn more efficiently and effectively by providing automatic immediate feedback on what the students have done so far. The evaluation of student work required for both of these roles can be relatively easy in domains like math, where there are clear right answers. When text is involved, however, automated evaluations become more difficult. Natural Language Processing (NLP) can provide quick evaluations of student texts. However, traditional neural network approaches require a large amount of data to train models with enough accuracy to be useful in analyzing student responses. Typically, educational studies collect data but often only in small amounts and with a narrow focus on a particular topic. BERT-based neural network models have revolutionized NLP because they are pre-trained on very large corpora, developing a robust, contextualized understanding of the language. Then they can be “fine-tuned” on a much smaller set of data for a particular task. However, these models still need a certain base level of training data to be reasonably accurate, and that base level can exceed that provided by educational applications, which might contain only a few dozen examples. In other areas of artificial intelligence, such as computer vision, model performance on small data sets has been improved by “data augmentation” — adding scaled and rotated versions of the original images to the training set. This has been attempted on textual data; however, augmenting text is much more difficult than simply scaling or rotating images. The newly generated sentences may not be semantically similar to the original sentence, resulting in an improperly trained model. In this paper, we examine a self-augmentation method that is straightforward and shows great improvements in performance with different BERT-based models in two different languages and on two different tasks that have small data sets. We also identify the limitations of the self-augmentation procedure. 
    more » « less