Multi-Intent Detection in User Provided Annotations for Programming by Examples Systems
In mapping enterprise applications, data mapping remains a fundamental part of integration development, but its time consuming. An increasing number of applications lack naming standards, and nested field structures further add complexity for the integration developers. Once the mapping is done, data transformation is the next challenge for the users since each application expects data to be in a certain format. Also, while building integration flow, developers need to understand the format of the source and target data field and come up with transformation program that can change data from source to target format. The problem of automatic generation of a transformation program through program synthesis paradigm from some specifications has been studied since the early days of Artificial Intelligence (AI). Programming by Example (PBE) is one such kind of technique that targets automatic inferencing of a computer program to accomplish a format or string conversion task from user-provided input and output samples. To learn the correct intent, a diverse set of samples from the user is required. However, there is a possibility that the user fails to provide a diverse set of samples. This can lead to multiple intents or ambiguity in the input and output samples. Hence, PBE systems can get confused in generating the correct intent program. In this paper, we propose a deep neural network based ambiguity prediction model, which analyzes the input-output strings and maps them to a different set of properties responsible for multiple intent. Users can analyze these properties and accordingly can provide new samples or modify existing samples which can help in building a better PBE system for mapping enterprise applications.
Code (0)
등록된 구현이 없습니다.
Tasks
Intent DetectionProgram SynthesisSimilar Papers 제목 키워드 기반
Joint Multiple Intent Detection and Slot Filling via Self-distillation
Intent detection and slot filling are two main tasks in natural language understanding (NLU) for identifying users' needs from their utterances. These two tasks are highly related and often trained jointly. However, most…
DecoderIntent DetectionMultiple Instance LearningNatural Language Understanding+2Bridging the Gap Between Object Detection and User Intent via Query-Modulation
When interacting with objects through cameras, or pictures, users often have a specific intent. For example, they may want to perform a visual search. With most object detection models relying on image pixels as their so…
Objectobject-detectionObject DetectionReferring ExpressionWeakly Supervised Video Anomaly Detection with Anomaly-Connected Components and Intention Reasoning
Weakly supervised video anomaly detection (WS-VAD) involves identifying the temporal intervals that contain anomalous events in untrimmed videos, where only video-level annotations are provided as supervisory signals. Ho…
Video Anomaly DetectionBETOLD: A Task-Oriented Dialog Dataset for Breakdown Detection
Task-Oriented Dialog (TOD) systems often suffer from dialog breakdowns - situations in which users cannot or do not want to proceed with the conversation. Ideally TOD systems should be able to detect dialog breakdowns to…
Privacy PreservingA Label-Aware BERT Attention Network for Zero-Shot Multi-Intent Detection in Spoken Language Understanding
With the early success of query-answer assistants such as Alexa and Siri, research attempts to expand system capabilities of handling service automation are now abundant. However, preliminary systems have quickly found t…
Intent DetectionSpoken Language Understanding