Fabian Suchanek

Also published as: Fabian M. Suchanek


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Using a Knowledge Base to Automatically Annotate Speech Corpora and to Identify Sociolinguistic Variation
Yaru Wu | Fabian Suchanek | Ioana Vasilescu | Lori Lamel | Martine Adda-Decker
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Speech characteristics vary from speaker to speaker. While some variation phenomena are due to the overall communication setting, others are due to diastratic factors such as gender, provenance, age, and social background. The analysis of these factors, although relevant for both linguistic and speech technology communities, is hampered by the need to annotate existing corpora or to recruit, categorise, and record volunteers as a function of targeted profiles. This paper presents a methodology that uses a knowledge base to provide speaker-specific information. This can facilitate the enrichment of existing corpora with new annotations extracted from the knowledge base. The method also helps the large scale analysis by automatically extracting instances of speech variation to correlate with diastratic features. We apply our method to an over 120-hour corpus of broadcast speech in French and investigate variation patterns linked to reduction phenomena and/or specific to connected speech such as disfluencies. We find significant differences in speech rate, the use of filler words, and the rate of non-canonical realisations of frequent segments as a function of different professional categories and age groups.

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Of Human Criteria and Automatic Metrics: A Benchmark of the Evaluation of Story Generation
Cyril Chhun | Pierre Colombo | Fabian M. Suchanek | Chloé Clavel
Proceedings of the 29th International Conference on Computational Linguistics

Research on Automatic Story Generation (ASG) relies heavily on human and automatic evaluation. However, there is no consensus on which human evaluation criteria to use, and no analysis of how well automatic criteria correlate with them. In this paper, we propose to re-evaluate ASG evaluation. We introduce a set of 6 orthogonal and comprehensive human criteria, carefully motivated by the social sciences literature. We also present HANNA, an annotated dataset of 1,056 stories produced by 10 different ASG systems. HANNA allows us to quantitatively evaluate the correlations of 72 automatic metrics with human criteria. Our analysis highlights the weaknesses of current metrics for ASG and allows us to formulate practical recommendations for ASG evaluation.

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Imputing Out-of-Vocabulary Embeddings with LOVE Makes LanguageModels Robust with Little Cost
Lihu Chen | Gael Varoquaux | Fabian Suchanek
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

State-of-the-art NLP systems represent inputs with word embeddings, but these are brittle when faced with Out-of-Vocabulary (OOV) words.To address this issue, we follow the principle of mimick-like models to generate vectors for unseen words, by learning the behavior of pre-trained embeddings using only the surface form of words.We present a simple contrastive learning framework, LOVE, which extends the word representation of an existing pre-trained language model (such as BERT) and makes it robust to OOV with few additional parameters.Extensive evaluations demonstrate that our lightweight model achieves similar or even better performances than prior competitors, both on original datasets and on corrupted variants. Moreover, it can be used in a plug-and-play fashion with FastText and BERT, where it significantly improves their robustness.


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But What Do We Actually Know?
Simon Razniewski | Fabian Suchanek | Werner Nutt
Proceedings of the 5th Workshop on Automated Knowledge Base Construction


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Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction (AKBC-WEKEX)
James Fan | Raphael Hoffman | Aditya Kalyanpur | Sebastian Riedel | Fabian Suchanek | Partha Pratim Talukdar
Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction (AKBC-WEKEX)

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PATTY: A Taxonomy of Relational Patterns with Semantic Types
Ndapandula Nakashole | Gerhard Weikum | Fabian Suchanek
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning


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LEILA: Learning to Extract Information by Linguistic Analysis
Fabian M. Suchanek | Georgiana Ifrim | Gerhard Weikum
Proceedings of the 2nd Workshop on Ontology Learning and Population: Bridging the Gap between Text and Knowledge