Kay Rottmann


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MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages
Jack FitzGerald | Christopher Hench | Charith Peris | Scott Mackie | Kay Rottmann | Ana Sanchez | Aaron Nash | Liam Urbach | Vishesh Kakarala | Richa Singh | Swetha Ranganath | Laurie Crist | Misha Britan | Wouter Leeuwis | Gokhan Tur | Prem Natarajan
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present the MASSIVE dataset–Multilingual Amazon Slu resource package (SLURP) for Slot-filling, Intent classification, and Virtual assistant Evaluation. MASSIVE contains 1M realistic, parallel, labeled virtual assistant utterances spanning 51 languages, 18 domains, 60 intents, and 55 slots. MASSIVE was created by tasking professional translators to localize the English-only SLURP dataset into 50 typologically diverse languages from 29 genera. We also present modeling results on XLM-R and mT5, including exact match accuracy, intent classification accuracy, and slot-filling F1 score. We have released our dataset, modeling code, and models publicly.

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Mitigating the Burden of Redundant Datasets via Batch-Wise Unique Samples and Frequency-Aware Losses
Donato Crisostomi | Andrea Caciolai | Alessandro Pedrani | Kay Rottmann | Alessandro Manzotti | Enrico Palumbo | Davide Bernardi
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)

Datasets used to train deep learning models in industrial settings often exhibit skewed distributions with some samples repeated a large number of times. This paper presents a simple yet effective solution to reduce the increased burden of repeated computation on redundant datasets. Our approach eliminates duplicates at the batch level, without altering the data distribution observed by the model, making it model-agnostic and easy to implement as a plug-and-play module. We also provide a mathematical expression to estimate the reduction in training time that our approach provides. Through empirical evidence, we show that our approach significantly reduces training times on various models across datasets with varying redundancy factors, without impacting their performance on the Named Entity Recognition task, both on publicly available datasets and in real industrial settings. In the latter, the approach speeds training by up to 87%, and by 46% on average, with a drop in model performance of 0.2% relative at worst. We finally release a modular and reusable codebase to further advance research in this area.


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Unsupervised training data re-weighting for natural language understanding with local distribution approximation
Jose Garrido Ramas | Dieu-thu Le | Bei Chen | Manoj Kumar | Kay Rottmann
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track

One of the major challenges of training Natural Language Understanding (NLU) production models lies in the discrepancy between the distributions of the offline training data and of the online live data, due to, e.g., biased sampling scheme, cyclic seasonality shifts, annotated training data coming from a variety of different sources, and a changing pool of users. Consequently, the model trained by the offline data is biased. We often observe this problem especially in task-oriented conversational systems, where topics of interest and the characteristics of users using the system change over time. In this paper we propose an unsupervised approach to mitigate the offline training data sampling bias in multiple NLU tasks. We show that a local distribution approximation in the pre-trained embedding space enables the estimation of importance weights for training samples guiding re-sampling for an effective bias mitigation. We illustrate our novel approach using multiple NLU datasets and show improvements obtained without additional annotation, making this a general approach for mitigating effects of sampling bias.

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Semi-supervised Adversarial Text Generation based on Seq2Seq models
Hieu Le | Dieu-thu Le | Verena Weber | Chris Church | Kay Rottmann | Melanie Bradford | Peter Chin
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track

To improve deep learning models’ robustness, adversarial training has been frequently used in computer vision with satisfying results. However, adversarial perturbation on text have turned out to be more challenging due to the discrete nature of text. The generated adversarial text might not sound natural or does not preserve semantics, which is the key for real world applications where text classification is based on semantic meaning. In this paper, we describe a new way for generating adversarial samples by using pseudo-labeled in-domain text data to train a seq2seq model for adversarial generation and combine it with paraphrase detection. We showcase the benefit of our approach for a real-world Natural Language Understanding (NLU) task, which maps a user’s request to an intent. Furthermore, we experiment with gradient-based training for the NLU task and try using token importance scores to guide the adversarial text generation. We show that our approach can generate realistic and relevant adversarial samples compared to other state-of-the-art adversarial training methods. Applying adversarial training using these generated samples helps the NLU model to recover up to 70% of these types of errors and makes the model more robust, especially in the tail distribution in a large scale real world application.

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Proceedings of the Massively Multilingual Natural Language Understanding Workshop (MMNLU-22)
Jack FitzGerald | Kay Rottmann | Julia Hirschberg | Mohit Bansal | Anna Rumshisky | Charith Peris | Christopher Hench
Proceedings of the Massively Multilingual Natural Language Understanding Workshop (MMNLU-22)

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Massively Multilingual Natural Language Understanding 2022 (MMNLU-22) Workshop and Competition
Jack FitzGerald | Christopher Hench | Charith Peris | Kay Rottmann
Proceedings of the Massively Multilingual Natural Language Understanding Workshop (MMNLU-22)

To be writen (workshop summary paper)


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Training data reduction for multilingual Spoken Language Understanding systems
Anmol Bansal | Anjali Shenoy | Krishna Chaitanya Pappu | Kay Rottmann | Anurag Dwarakanath
Proceedings of the 18th International Conference on Natural Language Processing (ICON)

Fine-tuning self-supervised pre-trained language models such as BERT has significantly improved state-of-the-art performance on natural language processing tasks. Similar finetuning setups can also be used in commercial large scale Spoken Language Understanding (SLU) systems to perform intent classification and slot tagging on user queries. Finetuning such powerful models for use in commercial systems requires large amounts of training data and compute resources to achieve high performance. This paper is a study on the different empirical methods of identifying training data redundancies for the fine tuning paradigm. Particularly, we explore rule based and semantic techniques to reduce data in a multilingual fine tuning setting and report our results on key SLU metrics. Through our experiments, we show that we can achieve on par/better performance on fine-tuning using a reduced data set as compared to a model finetuned on the entire data set.


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Tools for Collecting Speech Corpora via Mechanical-Turk
Ian Lane | Matthias Eck | Kay Rottmann | Alex Waibel
Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk


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Recent Improvements in the CMU Large Scale Chinese-English SMT System
Almut Silja Hildebrand | Kay Rottmann | Mohamed Noamany | Quin Gao | Sanjika Hewavitharana | Nguyen Bach | Stephan Vogel
Proceedings of ACL-08: HLT, Short Papers


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The CMU-UKA statistical machine translation systems for IWSLT 2007
Ian Lane | Andreas Zollmann | Thuy Linh Nguyen | Nguyen Bach | Ashish Venugopal | Stephan Vogel | Kay Rottmann | Ying Zhang | Alex Waibel
Proceedings of the Fourth International Workshop on Spoken Language Translation

This paper describes the CMU-UKA statistical machine translation systems submitted to the IWSLT 2007 evaluation campaign. Systems were submitted for three language-pairs: Japanese→English, Chinese→English and Arabic→English. All systems were based on a common phrase-based SMT (statistical machine translation) framework but for each language-pair a specific research problem was tackled. For Japanese→English we focused on two problems: first, punctuation recovery, and second, how to incorporate topic-knowledge into the translation framework. Our Chinese→English submission focused on syntax-augmented SMT and for the Arabic→English task we focused on incorporating morphological-decomposition into the SMT framework. This research strategy enabled us to evaluate a wide variety of approaches which proved effective for the language pairs they were evaluated on.

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The ISL Phrase-Based MT System for the 2007 ACL Workshop on Statistical Machine Translation
Matthias Paulik | Kay Rottmann | Jan Niehues | Silja Hildebrand | Stephan Vogel
Proceedings of the Second Workshop on Statistical Machine Translation

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Word reordering in statistical machine translation with a POS-based distortion model
Kay Rottmann | Stephan Vogel
Proceedings of the 11th Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages: Papers