Polyglot Semantic Parsing in APIs

Traditional approaches to semantic parsing (SP) work by training individual models for each available parallel dataset of text-meaning pairs. In this paper, we explore the idea of polyglot semantic translation, or learning semantic parsing models that are trained on multiple datasets and natural languages. In particular, we focus on translating text to code signature representations using the software component datasets of Richardson and Kuhn (2017a,b). The advantage of such models is that they can be used for parsing a wide variety of input natural languages and output programming languages, or mixed input languages, using a single unified model. To facilitate modeling of this type, we develop a novel graph-based decoding framework that achieves state-of-the-art performance on the above datasets, and apply this method to two other benchmark SP tasks.Comment: accepted for NAACL-2018 (camera ready version

Evaluating GPT-4 and ChatGPT on Japanese Medical Licensing Examinations

As large language models (LLMs) gain popularity among speakers of diverse languages, we believe that it is crucial to benchmark them to better understand model behaviors, failures, and limitations in languages beyond English. In this work, we evaluate LLM APIs (ChatGPT, GPT-3, and GPT-4) on the Japanese national medical licensing examinations from the past five years, including the current year. Our team comprises native Japanese-speaking NLP researchers and a practicing cardiologist based in Japan. Our experiments show that GPT-4 outperforms ChatGPT and GPT-3 and passes all six years of the exams, highlighting LLMs' potential in a language that is typologically distant from English. However, our evaluation also exposes critical limitations of the current LLM APIs. First, LLMs sometimes select prohibited choices that should be strictly avoided in medical practice in Japan, such as suggesting euthanasia. Further, our analysis shows that the API costs are generally higher and the maximum context size is smaller for Japanese because of the way non-Latin scripts are currently tokenized in the pipeline. We release our benchmark as Igaku QA as well as all model outputs and exam metadata. We hope that our results and benchmark will spur progress on more diverse applications of LLMs. Our benchmark is available at https://github.com/jungokasai/IgakuQA.Comment: Added results from the March 2023 exa

New resources and ideas for semantic parser induction

In this thesis, we investigate the general topic of computational natural language understanding (NLU), which has as its goal the development of algorithms and other computational methods that support reasoning about natural language by the computer. Under the classical approach, NLU models work similar to computer compilers (Aho et al., 1986), and include as a central component a semantic parser that translates natural language input (i.e., the compiler’s high-level language) to lower-level formal languages that facilitate program execution and exact reasoning. Given the difficulty of building natural language compilers by hand, recent work has centered around semantic parser induction, or on using machine learning to learn semantic parsers and semantic representations from parallel data consisting of example text-meaning pairs (Mooney, 2007a). One inherent difficulty in this data-driven approach is finding the parallel data needed to train the target semantic parsing models, given that such data does not occur naturally “in the wild” (Halevy et al., 2009). Even when data is available, the amount of domain- and language-specific data and the nature of the available annotations might be insufficient for robust machine learning and capturing the full range of NLU phenomena. Given these underlying resource issues, the semantic parsing field is in constant need of new resources and datasets, as well as novel learning techniques and task evaluations that make models more robust and adaptable to the many applications that require reliable semantic parsing. To address the main resource problem involving finding parallel data, we investigate the idea of using source code libraries, or collections of code and text documentation, as a parallel corpus for semantic parser development and introduce 45 new datasets in this domain and a new and challenging text-to-code translation task. As a way of addressing the lack of domain- and language-specific parallel data, we then use these and other benchmark datasets to investigate training se- mantic parsers on multiple datasets, which helps semantic parsers to generalize across different domains and languages and solve new tasks such as polyglot decoding and zero-shot translation (i.e., translating over and between multiple natural and formal languages and unobserved language pairs). Finally, to address the issue of insufficient annotations, we introduce a new learning framework called learning from entailment that uses entailment information (i.e., high-level inferences about whether the meaning of one sentence follows from another) as a weak learning signal to train semantic parsers to reason about the holes in their analysis and learn improved semantic representations. Taken together, this thesis contributes a wide range of new techniques and technical solutions to help build semantic parsing models with minimal amounts of training supervision and manual engineering effort, hence avoiding the resource issues described at the onset. We also introduce a diverse set of new NLU tasks for evaluating semantic parsing models, which we believe help to extend the scope and real world applicability of semantic parsing and computational NLU

Execution vs Parse-based Language Servers: Tradeoffs and Opportunities for Language-Agnostic Tooling for Dynamic Languages

With the wide adoption of the language server protocol, the desire to have IDE-style tooling even for niche and research languages has exploded. The Truffle language framework facilitates this desire by offering an almost zero-effort approach to language implementers to providing IDE features. However, this existing approach needs to execute the code being worked on to capture much of the information needed for an IDE, ideally with full unit-test coverage. To capture information more reliably and avoid the need to execute the code being worked on, we propose a new parse-based design for language servers. Our solution provides a language-agnostic interface for structural information, with which we can support most common IDE features for dynamic languages. Comparing the two approaches, we find that our new parse-based approach requires only a modest development effort for each language and has only minor tradeoffs for precision, for instance for code completion, compared to Truffle's execution-based approach. Further, we show that less than 1,000 lines of code capture enough details to provide much of the typical IDE functionality, with an order of magnitude less code than ad hoc language servers. We tested our approach for the custom parsers of Newspeak and SOM, as well as SimpleLanguage's ANTLR grammar without any changes to it. Combining both parse and execution-based approaches has the potential to provide good and precise IDE tooling for a wide range of languages with only small development effort. By itself, our approach would be a good addition to the many libraries implementing the language server protocol to enable low-effort implementations of IDE features

Digital Creativity Support for Original Journalism

The decline in circulations and revenues resulting from the digitalization of news production and consumption has led to a crisis in journalism.Journalists have less time to research, investigate and write original stories, leading to problems for our democratic processes and holding the powerful to account. This paper reports the architecture, features and rationale for new digital creativity support designed to support journalists to discover more original angles onstories. It also summarises the evaluation of the tool’s use in 3 newsrooms