Gunta Nešpore-Bērzkalne


Domain Expert Platform for Goal-Oriented Dialog Collection
Didzis Goško | Arturs Znotins | Inguna Skadina | Normunds Gruzitis | Gunta Nešpore-Bērzkalne
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

Today, most dialogue systems are fully or partly built using neural network architectures. A crucial prerequisite for the creation of a goal-oriented neural network dialogue system is a dataset that represents typical dialogue scenarios and includes various semantic annotations, e.g. intents, slots and dialogue actions, that are necessary for training a particular neural network architecture. In this demonstration paper, we present an easy to use interface and its back-end which is oriented to domain experts for the collection of goal-oriented dialogue samples. The platform not only allows to collect or write sample dialogues in a structured way, but also provides a means for simple annotation and interpretation of the dialogues. The platform itself is language-independent; it depends only on the availability of particular language processing components for a specific language. It is currently being used to collect dialogue samples in Latvian (a highly inflected language) which represent typical communication between students and the student service.


Deriving a PropBank Corpus from Parallel FrameNet and UD Corpora
Normunds Gruzitis | Roberts Darģis | Laura Rituma | Gunta Nešpore-Bērzkalne | Baiba Saulite
Proceedings of the International FrameNet Workshop 2020: Towards a Global, Multilingual FrameNet

We propose an approach for generating an accurate and consistent PropBank-annotated corpus, given a FrameNet-annotated corpus which has an underlying dependency annotation layer, namely, a parallel Universal Dependencies (UD) treebank. The PropBank annotation layer of such a multi-layer corpus can be semi-automatically derived from the existing FrameNet and UD annotation layers, by providing a mapping configuration from lexical units in [a non-English language] FrameNet to [English language] PropBank predicates, and a mapping configuration from FrameNet frame elements to PropBank semantic arguments for the given pair of a FrameNet frame and a PropBank predicate. The latter mapping generally depends on the underlying UD syntactic relations. To demonstrate our approach, we use Latvian FrameNet, annotated on top of Latvian UD Treebank, for generating Latvian PropBank in compliance with the Universal Propositions approach.