The ParlaSent Multilingual Training Dataset for Sentiment Identification in Parliamentary Proceedings
- URL: http://arxiv.org/abs/2309.09783v2
- Date: Wed, 20 Mar 2024 10:33:24 GMT
- Title: The ParlaSent Multilingual Training Dataset for Sentiment Identification in Parliamentary Proceedings
- Authors: Michal Mochtak, Peter Rupnik, Nikola Ljubešić,
- Abstract summary: The paper presents a new training dataset of sentences in 7 languages, manually annotated for sentiment.
The paper additionally introduces the first domain-specific multilingual transformer language model for political science applications.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The paper presents a new training dataset of sentences in 7 languages, manually annotated for sentiment, which are used in a series of experiments focused on training a robust sentiment identifier for parliamentary proceedings. The paper additionally introduces the first domain-specific multilingual transformer language model for political science applications, which was additionally pre-trained on 1.72 billion words from parliamentary proceedings of 27 European parliaments. We present experiments demonstrating how the additional pre-training on parliamentary data can significantly improve the model downstream performance, in our case, sentiment identification in parliamentary proceedings. We further show that our multilingual model performs very well on languages not seen during fine-tuning, and that additional fine-tuning data from other languages significantly improves the target parliament's results. The paper makes an important contribution to multiple disciplines inside the social sciences, and bridges them with computer science and computational linguistics. Lastly, the resulting fine-tuned language model sets up a more robust approach to sentiment analysis of political texts across languages, which allows scholars to study political sentiment from a comparative perspective using standardized tools and techniques.
Related papers
- The ParlaSpeech Collection of Automatically Generated Speech and Text Datasets from Parliamentary Proceedings [0.0]
We present our approach to building large and open speech-and-text-aligned datasets of less-resourced languages.
We focus on three Slavic languages, namely Croatian, Polish, and Serbian.
The results of this pilot run are three high-quality datasets that span more than 5,000 hours of speech and accompanying text transcripts.
arXiv Detail & Related papers (2024-09-23T10:12:18Z) - Learning Phonotactics from Linguistic Informants [54.086544221761486]
Our model iteratively selects or synthesizes a data-point according to one of a range of information-theoretic policies.
We find that the information-theoretic policies that our model uses to select items to query the informant achieve sample efficiency comparable to, or greater than, fully supervised approaches.
arXiv Detail & Related papers (2024-05-08T00:18:56Z) - Multi-EuP: The Multilingual European Parliament Dataset for Analysis of
Bias in Information Retrieval [62.82448161570428]
This dataset is designed to investigate fairness in a multilingual information retrieval context.
It boasts an authentic multilingual corpus, featuring topics translated into all 24 languages.
It offers rich demographic information associated with its documents, facilitating the study of demographic bias.
arXiv Detail & Related papers (2023-11-03T12:29:11Z) - Towards a Deep Understanding of Multilingual End-to-End Speech
Translation [52.26739715012842]
We analyze representations learnt in a multilingual end-to-end speech translation model trained over 22 languages.
We derive three major findings from our analysis.
arXiv Detail & Related papers (2023-10-31T13:50:55Z) - Political corpus creation through automatic speech recognition on EU
debates [4.670305538969914]
We present a transcribed corpus of the LIBE committee of the EU parliament, totalling 3.6 Million running words.
The meetings of parliamentary committees of the EU are a potentially valuable source of information for political scientists but the data is not readily available because only disclosed as speech recordings together with limited metadata.
We investigated the most appropriate Automatic Speech Recognition (ASR) model to create an accurate text transcription of the audio recordings of the meetings in order to make their content available for research and analysis.
arXiv Detail & Related papers (2023-04-17T10:41:59Z) - A Commonsense-Infused Language-Agnostic Learning Framework for Enhancing
Prediction of Political Polarity in Multilingual News Headlines [0.0]
We use the method of translation and retrieval to acquire the inferential knowledge in the target language.
We then employ an attention mechanism to emphasise important inferences.
We present a dataset of over 62.6K multilingual news headlines in five European languages annotated with their respective political polarities.
arXiv Detail & Related papers (2022-12-01T06:07:01Z) - Exploring Teacher-Student Learning Approach for Multi-lingual
Speech-to-Intent Classification [73.5497360800395]
We develop an end-to-end system that supports multiple languages.
We exploit knowledge from a pre-trained multi-lingual natural language processing model.
arXiv Detail & Related papers (2021-09-28T04:43:11Z) - A Massively Multilingual Analysis of Cross-linguality in Shared
Embedding Space [61.18554842370824]
In cross-lingual language models, representations for many different languages live in the same space.
We compute a task-based measure of cross-lingual alignment in the form of bitext retrieval performance.
We examine a range of linguistic, quasi-linguistic, and training-related features as potential predictors of these alignment metrics.
arXiv Detail & Related papers (2021-09-13T21:05:37Z) - Multilingual Neural RST Discourse Parsing [24.986030179701405]
We investigate two approaches to establish a neural, cross-lingual discourse via multilingual vector representations and segment-level translation.
Experiment results show that both methods are effective even with limited training data, and achieve state-of-the-art performance on cross-lingual, document-level discourse parsing.
arXiv Detail & Related papers (2020-12-03T05:03:38Z) - On the Language Neutrality of Pre-trained Multilingual Representations [70.93503607755055]
We investigate the language-neutrality of multilingual contextual embeddings directly and with respect to lexical semantics.
Our results show that contextual embeddings are more language-neutral and, in general, more informative than aligned static word-type embeddings.
We show how to reach state-of-the-art accuracy on language identification and match the performance of statistical methods for word alignment of parallel sentences.
arXiv Detail & Related papers (2020-04-09T19:50:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.