Motivated by the lack of data for non-English languages, in particular for the evaluation of downstream tasks such as Question Answering, we present a participatory effort to collect a native French Question Answering Dataset. Furthermore, we describe and publicly release the annotation tool developed for our collection effort, along with the data obtained and preliminary baselines.
Recent advances in the field of language modeling have improved state-of-the-art results on many Natural Language Processing tasks. Among them, Reading Comprehension has made significant progress over the past few years. However, most results are reported in English since labeled resources available in other languages, such as French, remain scarce. In the present work, we introduce the French Question Answering Dataset (FQuAD). FQuAD is a French Native Reading Comprehension dataset of questions and answers on a set of Wikipedia articles that consists of 25,000+ samples for the 1.0 version and…
Question Answering, including Reading Comprehension, is one of the NLP research areas that has seen significant scientific breakthroughs over the past few years, thanks to the concomitant advances in Language Modeling. Most of these breakthroughs, however, are centered on the English language. In 2020, as a first strong initiative to bridge the gap to the French language, Illuin Technology introduced FQuAD1.1, a French Native Reading Comprehension dataset composed of 60,000+ questions and answers samples extracted from Wikipedia articles. Nonetheless, Question Answering models trained on this …
Statutory article retrieval is the task of automatically retrieving law articles relevant to a legal question. While recent advances in natural language processing have sparked considerable interest in many legal tasks, statutory article retrieval remains primarily untouched due to the scarcity of large-scale and high-quality annotated datasets. To address this bottleneck, we introduce the Belgian Statutory Article Retrieval Dataset (BSARD), which consists of 1,100+ French native legal questions labeled by experienced jurists with relevant articles from a corpus of 22,600+ Belgian law articles…
University of Lisbon（里斯本大学）；University of Wolverhampton（伍尔弗汉普顿大学）；Harvard Medical School（哈佛医学院）；Macquarie University（麦考瑞大学）
In this paper we present NLI-PT, the first Portuguese dataset compiled for Native Language Identification (NLI), the task of identifying an author's first language based on their second language writing. The dataset includes 1,868 student essays written by learners of European Portuguese, native speakers of the following L1s: Chinese, English, Spanish, German, Russian, French, Japanese, Italian, Dutch, Tetum, Arabic, Polish, Korean, Romanian, and Swedish. NLI-PT includes the original student text and four different types of annotation: POS, fine-grained POS, constituency parses, and dependency…
Language Proficiency and F0 Entrainment: A Study of L2 English Imitation in Italian, French, and Slovak Speakers
Italian Institute of Technology（意大利技术研究院）；University of Ferrara（费拉拉大学）；Constantine the Philosopher University in Nitra（尼特拉君士坦丁哲学家大学）；Institute of Informatics, SAS（斯洛伐克科学院信息学研究所）
Histoires Morales: A French Dataset for Assessing Moral Alignment
Laboratoire Hubert Curien（于贝尔·屈里安实验室）；CNRS（法国国家科学研究中心）；Université Lumière Lyon 2（里昂第二大学）；Université Claude Bernard Lyon 1（里昂第一大学）；ERIC（信息、表示与智能认知研究室）；Télécom Paris（巴黎高等电信学院）；Institut Polytechnique de Paris（巴黎理工学院）
University of Edinburgh（爱丁堡大学）；LIMSI（法国计算机科学与自动化研究所）；CNRS（法国国家科学研究中心）；Université Paris-Saclay（巴黎萨克雷大学）；Univ. Paris-Sud（巴黎南大学）
We present a new English-French test set for the evaluation of Machine Translation (MT) for informal, written bilingual dialogue. The test set contains 144 spontaneous dialogues (5,700+ sentences) between native English and French speakers, mediated by one of two neural MT systems in a range of role-play settings. The dialogues are accompanied by fine-grained sentence-level judgments of MT quality, produced by the dialogue participants themselves, as well as by manually normalised versions and reference translations produced a posteriori. The motivation for the corpus is two-fold: to provide (…
In this paper, we present a data set and methods to compare speech processing models and human behaviour on a phone discrimination task. We provide Perceptimatic, an open data set which consists of French and English speech stimuli, as well as the results of 91 English- and 93 French-speaking listeners. The stimuli test a wide range of French and English contrasts, and are extracted directly from corpora of natural running read speech, used for the 2017 Zero Resource Speech Challenge. We provide a method to compare humans' perceptual space with models' representational space, and we apply it t…
University College Dublin（都柏林大学学院）；ADAPT SFI Research Centre（ADAPT SFI研究中心）
The objective of this work is to investigate complementary features which can aid the quintessential Mel frequency cepstral coefficients (MFCCs) in the task of closed, limited set word recognition for non-native English speakers of different mother-tongues. Unlike the MFCCs, which are derived from the spectral energy of the speech signal, the proposed frequency-centroids (FCs) encapsulate the spectral centres of the different bands of the speech spectrum, with the bands defined by the Mel filterbank. These features, in combination with the MFCCs, are observed to provide relative performance im…
ENS（巴黎高等师范学院）；CNRS（法国国家科学研究中心）；EHESS（社会科学高等学院）；INRIA（法国国家信息与自动化研究所）；University of Paris（巴黎大学）；University of Toronto（多伦多大学）
Our native language influences the way we perceive speech sounds, affecting our ability to discriminate non-native sounds. We compare two ideas about the influence of the native language on speech perception: the Perceptual Assimilation Model, which appeals to a mental classification of sounds into native phoneme categories, versus the idea that rich, fine-grained phonetic representations tuned to the statistics of the native language, are sufficient. We operationalize this idea using representations from two state-of-the-art speech models, a Dirichlet process Gaussian mixture model and the mo…
Covertly improving intelligibility with data-driven adaptations of speech timing
School of Computing Science Simon Fraser University（西蒙菲莎大学计算机科学学院）；Université Marie et Louis Pasteur SUPMICROTECH CNRS institut FEMTO-ST（玛丽-路易斯·巴斯德大学、SUPMICROTECH、法国国家科学研究中心FEMTO-ST研究所）；Department of Linguistics Simon Fraser University（西蒙菲莎大学语言学系）
