From `Snippet-lects' to Doculects and Dialects: Leveraging Neural Representations of Speech for Placing Audio Signals in a Language Landscape

XLSR-53 a multilingual model of speech, builds a vector representation from audio, which allows for a range of computational treatments. The experiments reported here use this neural representation to estimate the degree of closeness between audio files, ultimately aiming to extract relevant linguistic properties. We use max-pooling to aggregate the neural representations from a "snippet-lect" (the speech in a 5-second audio snippet) to a "doculect" (the speech in a given resource), then to dialects and languages. We use data from corpora of 11 dialects belonging to 5 less-studied languages. Similarity measurements between the 11 corpora bring out greatest closeness between those that are known to be dialects of the same language. The findings suggest that (i) dialect/language can emerge among the various parameters characterizing audio files and (ii) estimates of overall phonetic/phonological closeness can be obtained for a little-resourced or fully unknown language. The findings help shed light on the type of information captured by neural representations of speech and how it can be extracted from these representation

Using Artificial French Data to Understand the Emergence of Gender Bias in Transformer Language Models

Numerous studies have demonstrated the ability of neural language models to learn various linguistic properties without direct supervision. This work takes an initial step towards exploring the less researched topic of how neural models discover linguistic properties of words, such as gender, as well as the rules governing their usage. We propose to use an artificial corpus generated by a PCFG based on French to precisely control the gender distribution in the training data and determine under which conditions a model correctly captures gender information or, on the contrary, appears gender-biased.Comment: Accepted at EMNLP'2

Cross-lingual alignment transfer: a chicken-and-egg story?

International audienceIn this paper, we challenge a basic assumption of many cross-lingual transfer techniques: the availability of word aligned parallel corpora, and consider ways to accommodate situations in which such resources do not exist. We show experimentally that, here again, weakly supervised cross-lingual learning techniques can prove useful, once adapted to transfer knowledge across pairs of languages

A Comparison between NMT and PBSMT Performance for Translating Noisy User-Generated Content

International audienceThis work compares the performances achieved by Phrase-Based Statistical Ma- chine Translation systems (PBSMT) and attention-based Neural Machine Transla- tion systems (NMT) when translating User Generated Content (UGC), as encountered in social medias, from French to English. We show that, contrary to what could be ex- pected, PBSMT outperforms NMT when translating non-canonical inputs. Our error analysis uncovers the specificities of UGC that are problematic for sequential NMT architectures and suggests new avenue for improving NMT models

Les méthodes « apprendre à chercher » en traitement automatique des langues : un état de l'art

L'apprentissage structuré est au fondement des méthodes modernes d'apprentissage automatique pour le traitement automatique des langues (TAL). Dans cet article, nous étudions une famille d'algorithmes d'apprentissage structuré, les algorithmes de la famille « apprendre à  chercher », qui diffèrent fondamentalement des méthodes classiques telles que les champs markoviens aléatoires, et permettent donc de mettre en évidence certains des compromis de l'apprentissage structuré en TAL. Nous présentons également un panorama des applications de ces techniques en TAL, en discutant les bénéfices découlant de leur utilisation.L'apprentissage structuré est au fondement des méthodes modernes d'apprentissage automatique pour le traitement automatique des langues (TAL). Dans cet article, nous étudions une famille d'algorithmes d'apprentissage structuré, les algorithmes de la famille « apprendre à chercher », qui diffèrent fondamentalement des méthodes classiques telles que les champs markoviens aléatoires, et permettent donc de mettre en évidence certains des compromis de l'apprentissage structuré en TAL. Nous présentons également un panorama des applications de ces techniques en TAL, en discutant les bénéfices découlant de leur utilisation. ABSTRACT. Structured prediction lies at the heart of modern Natural language Processing (NLP). In this paper, we study a specific family of structured learning algorithms, loosely refered to as "Learning-to-search" algorithms. They differ in several important ways from more studied methods such as Conditional Random Fields, and their study highlights several important trade-offs of structured learning for NLP. We also present an overview of existing applications of these techniques to NLP problems and discuss their potential benefits. MOTS-CLÉS : traitement automatique des langues, apprentissage structuré, apprendre à chercher

Is the Language Familiarity Effect gradual ? A computational modelling approach

International audienceAccording to the Language Familiarity Effect (LFE), people are better at discriminating between speakers of their native language. Although this cognitive effect was largely studied in the literature, experiments have only been conducted on a limited number of language pairs and their results only show the presence of the effect without yielding a gradual measure that may vary across language pairs. In this work, we show that the computational model of LFE introduced by Thorburn, Feldman, and Schatz (2019) can address these two limitations. In a first experiment, we attest to this model's capacity to obtain a gradual measure of the LFE by replicating behavioural findings on native and accented speech. In a second experiment, we evaluate LFE on a large number of language pairs, including many which have never been tested on humans. We show that the effect is replicated across a wide array of languages, providing further evidence of its universality. Building on the gradual measure of LFE, we also show that languages belonging to the same family yield smaller scores, supporting the idea of an effect of language distance on LFE

A Comparison between NMT and PBSMT Performance for Translating Noisy User-Generated Content

International audienceThis work compares the performances achieved by Phrase-Based Statistical Ma- chine Translation systems (PBSMT) and attention-based Neural Machine Transla- tion systems (NMT) when translating User Generated Content (UGC), as encountered in social medias, from French to English. We show that, contrary to what could be ex- pected, PBSMT outperforms NMT when translating non-canonical inputs. Our error analysis uncovers the specificities of UGC that are problematic for sequential NMT architectures and suggests new avenue for improving NMT models

ProsAudit, a prosodic benchmark for self-supervised speech models

We present ProsAudit, a benchmark in English to assess structural prosodic knowledge in self-supervised learning (SSL) speech models. It consists of two subtasks, their corresponding metrics, and an evaluation dataset. In the protosyntax task, the model must correctly identify strong versus weak prosodic boundaries. In the lexical task, the model needs to correctly distinguish between pauses inserted between words and within words. We also provide human evaluation scores on this benchmark. We evaluated a series of SSL models and found that they were all able to perform above chance on both tasks, even when evaluated on an unseen language. However, non-native models performed significantly worse than native ones on the lexical task, highlighting the importance of lexical knowledge in this task. We also found a clear effect of size with models trained on more data performing better in the two subtasks.Comment: Accepted at Interspeech 2023. 4 pages + references, 1 figur