Generating Synthetic Data for Neural Keyword-to-Question Models

Search typically relies on keyword queries, but these are often semantically ambiguous. We propose to overcome this by offering users natural language questions, based on their keyword queries, to disambiguate their intent. This keyword-to-question task may be addressed using neural machine translation techniques. Neural translation models, however, require massive amounts of training data (keyword-question pairs), which is unavailable for this task. The main idea of this paper is to generate large amounts of synthetic training data from a small seed set of hand-labeled keyword-question pairs. Since natural language questions are available in large quantities, we develop models to automatically generate the corresponding keyword queries. Further, we introduce various filtering mechanisms to ensure that synthetic training data is of high quality. We demonstrate the feasibility of our approach using both automatic and manual evaluation. This is an extended version of the article published with the same title in the Proceedings of ICTIR'18.Comment: Extended version of ICTIR'18 full paper, 11 page

A Multi-channel Application Framework for Customer Care Service Using Best-First Search Technique

It has become imperative to find a solution to the dissatisfaction in response by mobile service providers when interacting with their customer care centres. Problems faced with Human to Human Interaction (H2H) between customer care centres and their customers include delayed response time, inconsistent solutions to questions or enquires and lack of dedicated access channels for interaction with customer care centres in some cases. This paper presents a framework and development techniques for a multi-channel application providing Human to System (H2S) interaction for customer care centre of a mobile telecommunication provider. The proposed solution is called Interactive Customer Service Agent (ICSA). Based on single-authoring, it will provide three media of interaction with the customer care centre of a mobile telecommunication operator: voice, phone and web browsing. A mathematical search technique called Best-First Search to generate accurate results in a search environmen

The acoustic, the digital and the body: a survey on musical instruments

This paper reports on a survey conducted in the autumn of 2006 with the objective to understand people's relationship to their musical tools. The survey focused on the question of embodiment and its different modalities in the fields of acoustic and digital instruments. The questions of control, instrumental entropy, limitations and creativity were addressed in relation to people's activities of playing, creating or modifying their instruments. The approach used in the survey was phenomenological, i.e. we were concerned with the experience of playing, composing for and designing digital or acoustic instruments. At the time of analysis, we had 209 replies from musicians, composers, engineers, designers, artists and others interested in this topic. The survey was mainly aimed at instrumentalists and people who create their own instruments or compositions in flexible audio programming environments such as SuperCollider, Pure Data, ChucK, Max/MSP, CSound, etc

Generating Natural Questions About an Image

There has been an explosion of work in the vision & language community during the past few years from image captioning to video transcription, and answering questions about images. These tasks have focused on literal descriptions of the image. To move beyond the literal, we choose to explore how questions about an image are often directed at commonsense inference and the abstract events evoked by objects in the image. In this paper, we introduce the novel task of Visual Question Generation (VQG), where the system is tasked with asking a natural and engaging question when shown an image. We provide three datasets which cover a variety of images from object-centric to event-centric, with considerably more abstract training data than provided to state-of-the-art captioning systems thus far. We train and test several generative and retrieval models to tackle the task of VQG. Evaluation results show that while such models ask reasonable questions for a variety of images, there is still a wide gap with human performance which motivates further work on connecting images with commonsense knowledge and pragmatics. Our proposed task offers a new challenge to the community which we hope furthers interest in exploring deeper connections between vision & language.Comment: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistic