Terminology databases are highly useful for the dissemination of specialized knowledge. In this paper we present TZOS, an online terminology database to work on Basque academic terminology collaboratively. We show how this resource integrates the Communicative Theory of Terminology, together with the methodological matters, how it is connected with real corpus GARATERM, which terminology issues arise when terms are collected and future perspectives. The main objectives of this work are to develop basic tools to research academic registers and make the terminology collected by expert users available to the community. Even though TZOS has been designed for an educational context, its flexible structure makes possible to extend it also to the professional area. In this way, we have built IZIBI-TZOS which is a Civil Engineering oriented version of TZOS. These resources are already publicly available, and the ongoing work is towards the interlinking with other lexical resources by applying linking data principles.

The goal of this work is to build conversational Question Answering (QA) interfaces for the large body of domain-specific information available in FAQ sites.We present DoQA, a dataset with 2,437 dialogues and 10,917 QA pairs.The dialogues are collected from three Stack Exchange sites using the Wizard of Oz method with crowdsourcing.Compared to previous work, DoQA comprises well-defined information needs, leading to more coherent and natural conversations with less factoid questions and is multi-domain.In addition, we introduce a more realistic information retrieval (IR) scenario where the system needs to find the answer in any of the FAQ documents.The results of an existing, strong, system show that, thanks to transfer learning from a Wikipedia QA dataset and fine tuning on a single FAQ domain, it is possible to build high quality conversational QA systems for FAQs without indomain training data.The good results carry over into the more challenging IR scenario.In both cases, there is still ample room for improvement, as indicated by the higher human upperbound.

The interaction of conversational systems with users poses an exciting opportunity for improving them after deployment, but little evidence has been provided of its feasibility.In most applications, users are not able to provide the correct answer to the system, but they are able to provide binary (correct, incorrect) feedback.In this paper we propose feedback-weighted learning based on importance sampling to improve upon an initial supervised system using binary user feedback.We perform simulated experiments on document classification (for development) and Conversational Question Answering datasets like QuAC and DoQA, where binary user feedback is derived from gold annotations.The results show that our method is able to improve over the initial supervised system, getting close to a fully-supervised system that has access to the same labeled examples in in-domain experiments (QuAC), and even matching in out-of-domain experiments (DoQA).Our work opens the prospect to exploit interactions with real users and improve conversational systems after deployment.

Conversational Question Answering (CQA) systems meet user information needs by having conversations with them, where answers to the questions are retrieved from text. There exist a variety of datasets for English, with tens of thousands of training examples, and pre-trained language models have allowed to obtain impressive results. The goal of our research is to test the performance of CQA systems under low-resource conditions which are common for most non-English languages: small amounts of native annotations and other limitations linked to low resource languages, like lack of crowdworkers or smaller wikipedias. We focus on the Basque language, and present the first non-English CQA dataset and results. Our experiments show that it is possible to obtain good results with low amounts of native data thanks to cross-lingual transfer, with quality comparable to those obtained for English. We also discovered that dialogue history models are not directly transferable to another language, calling for further research. The dataset is publicly available.

Current Information Retrieval systems for digital cultural heritage support only the actual search aspect of the information seeking process. This demonstration presents the second PATHS system which provides the exploration, analysis, and sense-making features to support the full information seeking process.