This paper discusses the design principles and procedures for creating a balanced corpus for research in computational literary studies, building on the experience of computational linguistics but adapting it to the specificities of the digital humanities. It showcases the development of the Metadata-enriched Polish Novel Corpus from the 19th and 20th centuries (19/20MetaPNC), consisting of 1,000 novels from 1854–1939, as an illustrative case and proposes a comprehensive workflow for the creation and reuse of literary corpora. What sets 19/20MetaPNC apart is its approach to balance, which considers the spatial dimension, the inclusion of non-canonical texts previously overlooked by other corpora, and the use of a complex, multi-stage metadata enrichment and verification process. Emphasis is placed on research-oriented metadata design, efficient data collection and data sharing according to the FAIR principles as well as 5- and 7-star data standards to increase the visibility and reusability of the corpus. A knowledge graph-based solution for the creation of exchangeable and machine-readable metadata describing corpora has been developed. For this purpose, metadata from bibliographic catalogs and other sources were transformed into Linked Data following the bibliodata LODification approach.

In a spoken dialogue system, an NLU model is preceded by a speech recognition system that can deteriorate the performance of natural language understanding.This paper proposes a method for investigating the impact of speech recognition errors on the performance of natural language understanding models.The proposed method combines the back transcription procedure with a fine-grained technique for categorizing the errors that affect the performance of NLU models.The method relies on the usage of synthesized speech for NLU evaluation.We show that the use of synthesized speech in place of audio recording does not change the outcomes of the presented technique in a significant way.

This paper describes a challenge on Conversational AI correctness with the goal to develop Natural Language Understanding models that are robust against speech recognition errors.The data for the competition consist of natural language utterances along with semantic frames that represent the commands targeted at a virtual assistant.The specification of the task is given along with the data preparation procedure and the evaluation rules.The baseline models for the task are discussed and the results of the competition are reported.

The paper investigates methods of compressing part-of-speech models that are developed for an on-device grapheme-to-phoneme conversion module. The performance of part-of-speech models is analyzed under different compression regimes. The evaluation is done with respect to French, German and Italian datasets that consist of TTS input prompts. The study shows that a proper selection of a compression method reduces the model size significantly without deteriorating the grapheme-to-phoneme conversion performance.

This paper describes a solution for the Noetic End-to-End Response Selection challenge – one of the tasks of the 7th Dialog System Technology Challenge. The goal of the task is to select the most appropriate continuation of a dialogue from a given set of responses. We approach this problem by building an ensemble of supervised neural network based classifiers and unsupervised similarity models. The dialogue continuation is selected according to a score that aggregates the rankings of candidate responses determined by the models in the ensemble.