A Corpus for Sentence-level Subjectivity Detection on English News Articles
- URL: http://arxiv.org/abs/2305.18034v3
- Date: Fri, 24 May 2024 12:17:28 GMT
- Title: A Corpus for Sentence-level Subjectivity Detection on English News Articles
- Authors: Francesco Antici, Andrea Galassi, Federico Ruggeri, Katerina Korre, Arianna Muti, Alessandra Bardi, Alice Fedotova, Alberto Barrón-Cedeño,
- Abstract summary: We use our guidelines to collect NewsSD-ENG, a corpus of 638 objective and 411 subjective sentences extracted from English news articles on controversial topics.
Our corpus paves the way for subjectivity detection in English without relying on language-specific tools, such as lexicons or machine translation.
- Score: 49.49218203204942
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We develop novel annotation guidelines for sentence-level subjectivity detection, which are not limited to language-specific cues. We use our guidelines to collect NewsSD-ENG, a corpus of 638 objective and 411 subjective sentences extracted from English news articles on controversial topics. Our corpus paves the way for subjectivity detection in English and across other languages without relying on language-specific tools, such as lexicons or machine translation. We evaluate state-of-the-art multilingual transformer-based models on the task in mono-, multi-, and cross-language settings. For this purpose, we re-annotate an existing Italian corpus. We observe that models trained in the multilingual setting achieve the best performance on the task.
Related papers
- Exploring syntactic information in sentence embeddings through multilingual subject-verb agreement [1.4335183427838039]
We take the approach of developing curated synthetic data on a large scale, with specific properties.
We use a new multiple-choice task and datasets, Blackbird Language Matrices, to focus on a specific grammatical structural phenomenon.
We show that despite having been trained on multilingual texts in a consistent manner, multilingual pretrained language models have language-specific differences.
arXiv Detail & Related papers (2024-09-10T14:58:55Z) - Tokenization Impacts Multilingual Language Modeling: Assessing
Vocabulary Allocation and Overlap Across Languages [3.716965622352967]
We propose new criteria to evaluate the quality of lexical representation and vocabulary overlap observed in sub-word tokenizers.
Our findings show that the overlap of vocabulary across languages can be actually detrimental to certain downstream tasks.
arXiv Detail & Related papers (2023-05-26T18:06:49Z) - Corpus-Guided Contrast Sets for Morphosyntactic Feature Detection in
Low-Resource English Varieties [3.3536302616846734]
We present a human-in-the-loop approach to generate and filter effective contrast sets via corpus-guided edits.
We show that our approach improves feature detection for both Indian English and African American English, demonstrate how it can assist linguistic research, and release our fine-tuned models for use by other researchers.
arXiv Detail & Related papers (2022-09-15T21:19:31Z) - Models and Datasets for Cross-Lingual Summarisation [78.56238251185214]
We present a cross-lingual summarisation corpus with long documents in a source language associated with multi-sentence summaries in a target language.
The corpus covers twelve language pairs and directions for four European languages, namely Czech, English, French and German.
We derive cross-lingual document-summary instances from Wikipedia by combining lead paragraphs and articles' bodies from language aligned Wikipedia titles.
arXiv Detail & Related papers (2022-02-19T11:55:40Z) - 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) - AM2iCo: Evaluating Word Meaning in Context across Low-ResourceLanguages
with Adversarial Examples [51.048234591165155]
We present AM2iCo, Adversarial and Multilingual Meaning in Context.
It aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts.
Results reveal that current SotA pretrained encoders substantially lag behind human performance.
arXiv Detail & Related papers (2021-04-17T20:23:45Z) - Bridging Linguistic Typology and Multilingual Machine Translation with
Multi-View Language Representations [83.27475281544868]
We use singular vector canonical correlation analysis to study what kind of information is induced from each source.
We observe that our representations embed typology and strengthen correlations with language relationships.
We then take advantage of our multi-view language vector space for multilingual machine translation, where we achieve competitive overall translation accuracy.
arXiv Detail & Related papers (2020-04-30T16:25:39Z) - Investigating Language Impact in Bilingual Approaches for Computational
Language Documentation [28.838960956506018]
This paper investigates how the choice of translation language affects the posterior documentation work.
We create 56 bilingual pairs that we apply to the task of low-resource unsupervised word segmentation and alignment.
Our results suggest that incorporating clues into the neural models' input representation increases their translation and alignment quality.
arXiv Detail & Related papers (2020-03-30T10:30:34Z) - On the Importance of Word Order Information in Cross-lingual Sequence
Labeling [80.65425412067464]
Cross-lingual models that fit into the word order of the source language might fail to handle target languages.
We investigate whether making models insensitive to the word order of the source language can improve the adaptation performance in target languages.
arXiv Detail & Related papers (2020-01-30T03:35:44Z)
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.