Towards OpenDomain CrossLanguage Question Answering
Notice bibliographique
Résumé
We present MATQAM (Multilingual Answer Triggering Question Answering Machine) a multilingual answer triggering open-domain QA system, focusing on answering questions whose answers might be in free texts in multiple languages within Wikipedia.Obtaining relevant information from the Web has become more challenging, since online communities and social media tend to confine people to bounded trends and ways of thinking. Due to the large amount of data available, getting the relevant information has become a more challenging task. Unlike in standard Information Retrieval (IR), Question Answering (QA) systems aim at retrieving the relevant answer(s) to a question expressed in natural language, instead of returning a list of documents. On the one hand, information is dispersed in different languages and needs to be gathered to get more knowledge. On the other hand, extracting answers from multilingual documents is a complicated task because natural languages follow diverse linguistic syntaxes and rules, especially for Semitic languages, such as Arabic. This project tackles open-domain QA using Wikipedia as source of knowledge by building a multilingual —Arabic, French, English— QA system. In order to obtain a collection of Wikipedia articles as well as questions in multiple languages, we extended an existing English dataset: WikiQA (Yang et al., 2015). We used the WikiTailor toolkit (Barrón-Cedeño et al., 2015) to build a comparable corpus form Wikipedia articles and to extract the corresponding articles in Arabic, French, and English. We used neural machine translation to generate the questions in the three languages as well. Our QA system consists of the three following modules. (i) Question processing consists of transforming a natural language question into a query and determining the expected type of the answer in order to define the retrieval mechanism for the extraction function. (ii) The document retrieval module consists of retrieving the most relevant documents from the search engines —in multiple languages— given the produced query. The purpose of this module is to identify the documents that may contain an answer to the question. It requires cross-language representations as well as machine translation technology to do that, as the question could be asked in Arabic, French or English and the answer could be in either of these languages. (iii) The answer identification module ranks specific text fragments that are plausible answers to the question. It first ranks the candidate text fragments in the different languages and, if they are found, they are combined into one consolidated answer. This is a variation of the cross-language QA scenario enabling answer triggering, where no concrete answer has to be provided, if it does not exist. In order to build our QA system, we extend an existing framework (Rücklé and Gurevych, 2017) integrating neural networks for answer selection. References Alberto Barrón-Cedeño, Cristina España Bonet, Josu Boldoba Trapote, and Luís Márquez Villodre. A Factory of Comparable Corpora from Wikipedia. In Proceedings of the Eighth Workshop on Building and Using Comparable Corpora, pages 3–13, Beijing, China, 2015. Association for Computational Linguistics. Andreas Rücklé and Iryna Gurevych. End-to-End Non-Factoid Question Answering with an Interactive Visualization of Neural Attention Weights. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics-System Demonstrations (ACL 2017), pages 19–24, Vancouver, Canada, August 2017. Association for Computational Linguistics. doi:10.18653/v1/P17-4004. URL http://aclweb.org/anthology/P17-4004 . Yi Yang, Wen-tau Yih, and Christopher Meek. WikiQA: A Challenge Dataset for Open-Domain Question Answering. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 2013–2018, Lisbon, Portugal, 2015.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».