Recently, open-domain question answering systems have begun to rely heavily on annotated datasets to train neural passage retrievers. However, manually annotating such datasets is both difficult and time-consuming, which limits their availability for less popular languages. In this work, we experiment with several methods for automatically collecting weakly labeled datasets and show how they affect the performance of the neural passage retrieval models. As a result of our work, we publish the MAUPQA dataset, consisting of nearly 400,000 question-passage pairs for Polish, as well as the HerBERT-QA neural retriever.

MAUPQA: Massive Automatically-created Polish Question Answering Dataset

Novel intent discovery automates the process of grouping similar messages (questions) to identify previously unknown intents. However, current research focuses on publicly available datasets which have only the question field and significantly differ from real-life datasets. This paper proposes methods to improve the intent discovery pipeline deployed in a large e-commerce platform. We show the benefit of pre-training language models on in-domain data: both self-supervised and with weak supervision. We also devise the best method to utilize the conversational structure (i.e., question and answer) of real-life datasets during fine-tuning for clustering tasks, which we call Conv. All our methods combined to fully utilize real-life datasets give up to 33pp performance boost over state-of-the-art Constrained Deep Adaptive Clustering (CDAC) model for question only. By comparison CDAC model for the question data only gives only up to 13pp performance boost over the naive baseline.

Going beyond research datasets: Novel intent discovery in the industry setting

**Organizers:** 
Jakub Piskorski, Polish Academy of Sciences, Poland
Bogdan Babych, Heidelberg University, Germany
Michał Marcińczuk, Wroclaw University of Science and Technology, Samurai Labs, Poland
Preslav Nakov, Mohamed bin Zayed University of Artificial Intelligence, United Arab Emirates
Maciej Ogrodniczuk, Polish Academy of Sciences, Poland
Senja Pollak, Jožef Stefan Institute, Slovenia
Pavel Přibáň, University of West Bohemia, Plzeň, Czech Republic
Josef Steinberger, University of West Bohemia, Plzeň, Czech Republic
Roman Yangarber, University of Helsinki, Finland
Piotr Rybak, Polish Academy of Sciences, Poland For more details, please visit our [workshop website](http://bsnlp.cs.helsinki.fi/index.html))

SlavNLP-2023: The 9th Biennial Workshop on Slavic NLP

workshop paper

### Welcome to the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 
Continuing its mission of expanding and involving the science community of all European countries, EACL had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

You need to register for the conference in order to access this site. Please visit https://2023.eacl.org/registration for more information.

EACL 2023

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

Please feel free to view the Findings papers at your convenience. You are welcome to leave your comment or a question in the Chat box.

Piotr Rybak

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MAUPQA: Massive Automatically-created Polish Question Answering Dataset

Going beyond research datasets: Novel intent discovery in the industry setting

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