Ajout de nouveaux mots dans un modèle de langage à l'aide des paramètres de mots connus ayant un comportement similaire

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Luisa Orosanu
Denis Jouvet

Résumé

Cet article présente une étude sur la façon d'ajouter automatiquement de nouveaux mots dans un modèle de langage sans le recycler ni l'adapter (ce qui nécessite beaucoup de nouvelles données). L'approche proposée consiste à trouver une liste de mots similaires pour chaque nouveau mot à ajouter dans le modèle de langage. Sur la base d'un petit ensemble de phrases contenant les nouveaux mots et d'un ensemble de nombres de n-grammes contenant les mots connus, nous recherchons les mots connus qui ont la distribution des voisins la plus similaire (des quelques mots voisins précédents et suivants) à celle des mots voisins. nouveaux mots. Les mots similaires sont déterminés grâce au calcul des divergences KL sur la distribution des mots voisins. Les valeurs des paramètres n-grammes associées aux mots similaires sont ensuite utilisées pour définir les valeurs des paramètres n-grammes des nouveaux mots. Dans le contexte de la reconnaissance vocale, l'évaluation des performances sur une tâche LVCSR montre l'intérêt de l'approche proposée.

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Comment citer
Orosanu, L., & Jouvet, D. (2016). Ajout de nouveaux mots dans un modèle de langage à l’aide des paramètres de mots connus ayant un comportement similaire. AL-Lisaniyyat, 22(2), 23-27. https://doi.org/10.61850/allj.v22i2.369
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Articles

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Title : Textual Data Selection For Language Modelling In The Scope Of Automatic Speech Recognition
Authors : Mezzoudj Freha . Langlois David . Jouvet Denis . Benyettou Abdelkader .

Abstract
The language model is an important module in many applications that produce natural language text, in particular speech recognition. Training of language models requires large amounts of textual data that matches with the target domain. Selection of target domain (or in-domain) data has been investigated in the past. For example [1] has proposed a criterion based on the difference of cross-entropy between models representing in-domain and non-domain-specific data. However evaluations were conducted using only two sources of data, one corresponding to the in-domain, and another one to generic data from which sentences are selected. In the scope of broadcast news and TV shows transcription systems, language models are built by interpolating several language models estimated from various data sources. This paper investigates the data selection process in this context of building interpolated language models for speech transcription. Results show that, in the selection process, the choice of the language models for representing in-domain and non-domain-specific data is critical. Moreover, it is better to apply the data selection only on some selected data sources. This way, the selection process leads to an improvement of 8.3 in terms of perplexity and 0.2% in terms of word-error rate on the French broadcast transcription task.
Keywords
data selection process- language models- speech transcription

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